all-MiniLM-L6-v2 trained on MEDI-MTEB triplets

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the NQ, pubmed, specter_train_triples, S2ORC_citations_abstracts, fever, gooaq_pairs, codesearchnet, wikihow, WikiAnswers, eli5_question_answer, amazon-qa, medmcqa, zeroshot, TriviaQA_pairs, PAQ_pairs, stackexchange_duplicate_questions_title-body_title-body, trex, flickr30k_captions, hotpotqa, task671_ambigqa_text_generation, task061_ropes_answer_generation, task285_imdb_answer_generation, task905_hate_speech_offensive_classification, task566_circa_classification, task184_snli_entailment_to_neutral_text_modification, task280_stereoset_classification_stereotype_type, task1599_smcalflow_classification, task1384_deal_or_no_dialog_classification, task591_sciq_answer_generation, task823_peixian-rtgender_sentiment_analysis, task023_cosmosqa_question_generation, task900_freebase_qa_category_classification, task924_event2mind_word_generation, task152_tomqa_find_location_easy_noise, task1368_healthfact_sentence_generation, task1661_super_glue_classification, task1187_politifact_classification, task1728_web_nlg_data_to_text, task112_asset_simple_sentence_identification, task1340_msr_text_compression_compression, task072_abductivenli_answer_generation, task1504_hatexplain_answer_generation, task684_online_privacy_policy_text_information_type_generation, task1290_xsum_summarization, task075_squad1.1_answer_generation, task1587_scifact_classification, task384_socialiqa_question_classification, task1555_scitail_answer_generation, task1532_daily_dialog_emotion_classification, task239_tweetqa_answer_generation, task596_mocha_question_generation, task1411_dart_subject_identification, task1359_numer_sense_answer_generation, task329_gap_classification, task220_rocstories_title_classification, task316_crows-pairs_classification_stereotype, task495_semeval_headline_classification, task1168_brown_coarse_pos_tagging, task348_squad2.0_unanswerable_question_generation, task049_multirc_questions_needed_to_answer, task1534_daily_dialog_question_classification, task322_jigsaw_classification_threat, task295_semeval_2020_task4_commonsense_reasoning, task186_snli_contradiction_to_entailment_text_modification, task034_winogrande_question_modification_object, task160_replace_letter_in_a_sentence, task469_mrqa_answer_generation, task105_story_cloze-rocstories_sentence_generation, task649_race_blank_question_generation, task1536_daily_dialog_happiness_classification, task683_online_privacy_policy_text_purpose_answer_generation, task024_cosmosqa_answer_generation, task584_udeps_eng_fine_pos_tagging, task066_timetravel_binary_consistency_classification, task413_mickey_en_sentence_perturbation_generation, task182_duorc_question_generation, task028_drop_answer_generation, task1601_webquestions_answer_generation, task1295_adversarial_qa_question_answering, task201_mnli_neutral_classification, task038_qasc_combined_fact, task293_storycommonsense_emotion_text_generation, task572_recipe_nlg_text_generation, task517_emo_classify_emotion_of_dialogue, task382_hybridqa_answer_generation, task176_break_decompose_questions, task1291_multi_news_summarization, task155_count_nouns_verbs, task031_winogrande_question_generation_object, task279_stereoset_classification_stereotype, task1336_peixian_equity_evaluation_corpus_gender_classifier, task508_scruples_dilemmas_more_ethical_isidentifiable, task518_emo_different_dialogue_emotions, task077_splash_explanation_to_sql, task923_event2mind_classifier, task470_mrqa_question_generation, task638_multi_woz_classification, task1412_web_questions_question_answering, task847_pubmedqa_question_generation, task678_ollie_actual_relationship_answer_generation, task290_tellmewhy_question_answerability, task575_air_dialogue_classification, task189_snli_neutral_to_contradiction_text_modification, task026_drop_question_generation, task162_count_words_starting_with_letter, task079_conala_concat_strings, task610_conllpp_ner, task046_miscellaneous_question_typing, task197_mnli_domain_answer_generation, task1325_qa_zre_question_generation_on_subject_relation, task430_senteval_subject_count, task672_nummersense, task402_grailqa_paraphrase_generation, task904_hate_speech_offensive_classification, task192_hotpotqa_sentence_generation, task069_abductivenli_classification, task574_air_dialogue_sentence_generation, task187_snli_entailment_to_contradiction_text_modification, task749_glucose_reverse_cause_emotion_detection, task1552_scitail_question_generation, task750_aqua_multiple_choice_answering, task327_jigsaw_classification_toxic, task1502_hatexplain_classification, task328_jigsaw_classification_insult, task304_numeric_fused_head_resolution, task1293_kilt_tasks_hotpotqa_question_answering, task216_rocstories_correct_answer_generation, task1326_qa_zre_question_generation_from_answer, task1338_peixian_equity_evaluation_corpus_sentiment_classifier, task1729_personachat_generate_next, task1202_atomic_classification_xneed, task400_paws_paraphrase_classification, task502_scruples_anecdotes_whoiswrong_verification, task088_identify_typo_verification, task221_rocstories_two_choice_classification, task200_mnli_entailment_classification, task074_squad1.1_question_generation, task581_socialiqa_question_generation, task1186_nne_hrngo_classification, task898_freebase_qa_answer_generation, task1408_dart_similarity_classification, task168_strategyqa_question_decomposition, task1357_xlsum_summary_generation, task390_torque_text_span_selection, task165_mcscript_question_answering_commonsense, task1533_daily_dialog_formal_classification, task002_quoref_answer_generation, task1297_qasc_question_answering, task305_jeopardy_answer_generation_normal, task029_winogrande_full_object, task1327_qa_zre_answer_generation_from_question, task326_jigsaw_classification_obscene, task1542_every_ith_element_from_starting, task570_recipe_nlg_ner_generation, task1409_dart_text_generation, task401_numeric_fused_head_reference, task846_pubmedqa_classification, task1712_poki_classification, task344_hybridqa_answer_generation, task875_emotion_classification, task1214_atomic_classification_xwant, task106_scruples_ethical_judgment, task238_iirc_answer_from_passage_answer_generation, task1391_winogrande_easy_answer_generation, task195_sentiment140_classification, task163_count_words_ending_with_letter, task579_socialiqa_classification, task569_recipe_nlg_text_generation, task1602_webquestion_question_genreation, task747_glucose_cause_emotion_detection, task219_rocstories_title_answer_generation, task178_quartz_question_answering, task103_facts2story_long_text_generation, task301_record_question_generation, task1369_healthfact_sentence_generation, task515_senteval_odd_word_out, task496_semeval_answer_generation, task1658_billsum_summarization, task1204_atomic_classification_hinderedby, task1392_superglue_multirc_answer_verification, task306_jeopardy_answer_generation_double, task1286_openbookqa_question_answering, task159_check_frequency_of_words_in_sentence_pair, task151_tomqa_find_location_easy_clean, task323_jigsaw_classification_sexually_explicit, task037_qasc_generate_related_fact, task027_drop_answer_type_generation, task1596_event2mind_text_generation_2, task141_odd-man-out_classification_category, task194_duorc_answer_generation, task679_hope_edi_english_text_classification, task246_dream_question_generation, task1195_disflqa_disfluent_to_fluent_conversion, task065_timetravel_consistent_sentence_classification, task351_winomt_classification_gender_identifiability_anti, task580_socialiqa_answer_generation, task583_udeps_eng_coarse_pos_tagging, task202_mnli_contradiction_classification, task222_rocstories_two_chioce_slotting_classification, task498_scruples_anecdotes_whoiswrong_classification, task067_abductivenli_answer_generation, task616_cola_classification, task286_olid_offense_judgment, task188_snli_neutral_to_entailment_text_modification, task223_quartz_explanation_generation, task820_protoqa_answer_generation, task196_sentiment140_answer_generation, task1678_mathqa_answer_selection, task349_squad2.0_answerable_unanswerable_question_classification, task154_tomqa_find_location_hard_noise, task333_hateeval_classification_hate_en, task235_iirc_question_from_subtext_answer_generation, task1554_scitail_classification, task210_logic2text_structured_text_generation, task035_winogrande_question_modification_person, task230_iirc_passage_classification, task1356_xlsum_title_generation, task1726_mathqa_correct_answer_generation, task302_record_classification, task380_boolq_yes_no_question, task212_logic2text_classification, task748_glucose_reverse_cause_event_detection, task834_mathdataset_classification, task350_winomt_classification_gender_identifiability_pro, task191_hotpotqa_question_generation, task236_iirc_question_from_passage_answer_generation, task217_rocstories_ordering_answer_generation, task568_circa_question_generation, task614_glucose_cause_event_detection, task361_spolin_yesand_prompt_response_classification, task421_persent_sentence_sentiment_classification, task203_mnli_sentence_generation, task420_persent_document_sentiment_classification, task153_tomqa_find_location_hard_clean, task346_hybridqa_classification, task1211_atomic_classification_hassubevent, task360_spolin_yesand_response_generation, task510_reddit_tifu_title_summarization, task511_reddit_tifu_long_text_summarization, task345_hybridqa_answer_generation, task270_csrg_counterfactual_context_generation, task307_jeopardy_answer_generation_final, task001_quoref_question_generation, task089_swap_words_verification, task1196_atomic_classification_oeffect, task080_piqa_answer_generation, task1598_nyc_long_text_generation, task240_tweetqa_question_generation, task615_moviesqa_answer_generation, task1347_glue_sts-b_similarity_classification, task114_is_the_given_word_longest, task292_storycommonsense_character_text_generation, task115_help_advice_classification, task431_senteval_object_count, task1360_numer_sense_multiple_choice_qa_generation, task177_para-nmt_paraphrasing, task132_dais_text_modification, task269_csrg_counterfactual_story_generation, task233_iirc_link_exists_classification, task161_count_words_containing_letter, task1205_atomic_classification_isafter, task571_recipe_nlg_ner_generation, task1292_yelp_review_full_text_categorization, task428_senteval_inversion, task311_race_question_generation, task429_senteval_tense, task403_creak_commonsense_inference, task929_products_reviews_classification, task582_naturalquestion_answer_generation, task237_iirc_answer_from_subtext_answer_generation, task050_multirc_answerability, task184_break_generate_question, task669_ambigqa_answer_generation, task169_strategyqa_sentence_generation, task500_scruples_anecdotes_title_generation, task241_tweetqa_classification, task1345_glue_qqp_question_paraprashing, task218_rocstories_swap_order_answer_generation, task613_politifact_text_generation, task1167_penn_treebank_coarse_pos_tagging, task1422_mathqa_physics, task247_dream_answer_generation, task199_mnli_classification, task164_mcscript_question_answering_text, task1541_agnews_classification, task516_senteval_conjoints_inversion, task294_storycommonsense_motiv_text_generation, task501_scruples_anecdotes_post_type_verification, task213_rocstories_correct_ending_classification, task821_protoqa_question_generation, task493_review_polarity_classification, task308_jeopardy_answer_generation_all, task1595_event2mind_text_generation_1, task040_qasc_question_generation, task231_iirc_link_classification, task1727_wiqa_what_is_the_effect, task578_curiosity_dialogs_answer_generation, task310_race_classification, task309_race_answer_generation, task379_agnews_topic_classification, task030_winogrande_full_person, task1540_parsed_pdfs_summarization, task039_qasc_find_overlapping_words, task1206_atomic_classification_isbefore, task157_count_vowels_and_consonants, task339_record_answer_generation, task453_swag_answer_generation, task848_pubmedqa_classification, task673_google_wellformed_query_classification, task676_ollie_relationship_answer_generation, task268_casehold_legal_answer_generation, task844_financial_phrasebank_classification, task330_gap_answer_generation, task595_mocha_answer_generation, task1285_kpa_keypoint_matching, task234_iirc_passage_line_answer_generation, task494_review_polarity_answer_generation, task670_ambigqa_question_generation, task289_gigaword_summarization, npr, nli, SimpleWiki, amazon_review_2018, ccnews_title_text, agnews, xsum, msmarco, yahoo_answers_title_answer, squad_pairs, wow, mteb-amazon_counterfactual-avs_triplets, mteb-amazon_massive_intent-avs_triplets, mteb-amazon_massive_scenario-avs_triplets, mteb-amazon_reviews_multi-avs_triplets, mteb-banking77-avs_triplets, mteb-emotion-avs_triplets, mteb-imdb-avs_triplets, mteb-mtop_domain-avs_triplets, mteb-mtop_intent-avs_triplets, mteb-toxic_conversations_50k-avs_triplets, mteb-tweet_sentiment_extraction-avs_triplets and covid-bing-query-gpt4-avs_triplets datasets. It maps sentences & paragraphs to a 4096-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: sentence-transformers/all-MiniLM-L6-v2
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 4096 dimensions
• Similarity Function: Cosine Similarity
• Training Datasets:
  • NQ
  • pubmed
  • specter_train_triples
  • S2ORC_citations_abstracts
  • fever
  • gooaq_pairs
  • codesearchnet
  • wikihow
  • WikiAnswers
  • eli5_question_answer
  • amazon-qa
  • medmcqa
  • zeroshot
  • TriviaQA_pairs
  • PAQ_pairs
  • stackexchange_duplicate_questions_title-body_title-body
  • trex
  • flickr30k_captions
  • hotpotqa
  • task671_ambigqa_text_generation
  • task061_ropes_answer_generation
  • task285_imdb_answer_generation
  • task905_hate_speech_offensive_classification
  • task566_circa_classification
  • task184_snli_entailment_to_neutral_text_modification
  • task280_stereoset_classification_stereotype_type
  • task1599_smcalflow_classification
  • task1384_deal_or_no_dialog_classification
  • task591_sciq_answer_generation
  • task823_peixian-rtgender_sentiment_analysis
  • task023_cosmosqa_question_generation
  • task900_freebase_qa_category_classification
  • task924_event2mind_word_generation
  • task152_tomqa_find_location_easy_noise
  • task1368_healthfact_sentence_generation
  • task1661_super_glue_classification
  • task1187_politifact_classification
  • task1728_web_nlg_data_to_text
  • task112_asset_simple_sentence_identification
  • task1340_msr_text_compression_compression
  • task072_abductivenli_answer_generation
  • task1504_hatexplain_answer_generation
  • task684_online_privacy_policy_text_information_type_generation
  • task1290_xsum_summarization
  • task075_squad1.1_answer_generation
  • task1587_scifact_classification
  • task384_socialiqa_question_classification
  • task1555_scitail_answer_generation
  • task1532_daily_dialog_emotion_classification
  • task239_tweetqa_answer_generation
  • task596_mocha_question_generation
  • task1411_dart_subject_identification
  • task1359_numer_sense_answer_generation
  • task329_gap_classification
  • task220_rocstories_title_classification
  • task316_crows-pairs_classification_stereotype
  • task495_semeval_headline_classification
  • task1168_brown_coarse_pos_tagging
  • task348_squad2.0_unanswerable_question_generation
  • task049_multirc_questions_needed_to_answer
  • task1534_daily_dialog_question_classification
  • task322_jigsaw_classification_threat
  • task295_semeval_2020_task4_commonsense_reasoning
  • task186_snli_contradiction_to_entailment_text_modification
  • task034_winogrande_question_modification_object
  • task160_replace_letter_in_a_sentence
  • task469_mrqa_answer_generation
  • task105_story_cloze-rocstories_sentence_generation
  • task649_race_blank_question_generation
  • task1536_daily_dialog_happiness_classification
  • task683_online_privacy_policy_text_purpose_answer_generation
  • task024_cosmosqa_answer_generation
  • task584_udeps_eng_fine_pos_tagging
  • task066_timetravel_binary_consistency_classification
  • task413_mickey_en_sentence_perturbation_generation
  • task182_duorc_question_generation
  • task028_drop_answer_generation
  • task1601_webquestions_answer_generation
  • task1295_adversarial_qa_question_answering
  • task201_mnli_neutral_classification
  • task038_qasc_combined_fact
  • task293_storycommonsense_emotion_text_generation
  • task572_recipe_nlg_text_generation
  • task517_emo_classify_emotion_of_dialogue
  • task382_hybridqa_answer_generation
  • task176_break_decompose_questions
  • task1291_multi_news_summarization
  • task155_count_nouns_verbs
  • task031_winogrande_question_generation_object
  • task279_stereoset_classification_stereotype
  • task1336_peixian_equity_evaluation_corpus_gender_classifier
  • task508_scruples_dilemmas_more_ethical_isidentifiable
  • task518_emo_different_dialogue_emotions
  • task077_splash_explanation_to_sql
  • task923_event2mind_classifier
  • task470_mrqa_question_generation
  • task638_multi_woz_classification
  • task1412_web_questions_question_answering
  • task847_pubmedqa_question_generation
  • task678_ollie_actual_relationship_answer_generation
  • task290_tellmewhy_question_answerability
  • task575_air_dialogue_classification
  • task189_snli_neutral_to_contradiction_text_modification
  • task026_drop_question_generation
  • task162_count_words_starting_with_letter
  • task079_conala_concat_strings
  • task610_conllpp_ner
  • task046_miscellaneous_question_typing
  • task197_mnli_domain_answer_generation
  • task1325_qa_zre_question_generation_on_subject_relation
  • task430_senteval_subject_count
  • task672_nummersense
  • task402_grailqa_paraphrase_generation
  • task904_hate_speech_offensive_classification
  • task192_hotpotqa_sentence_generation
  • task069_abductivenli_classification
  • task574_air_dialogue_sentence_generation
  • task187_snli_entailment_to_contradiction_text_modification
  • task749_glucose_reverse_cause_emotion_detection
  • task1552_scitail_question_generation
  • task750_aqua_multiple_choice_answering
  • task327_jigsaw_classification_toxic
  • task1502_hatexplain_classification
  • task328_jigsaw_classification_insult
  • task304_numeric_fused_head_resolution
  • task1293_kilt_tasks_hotpotqa_question_answering
  • task216_rocstories_correct_answer_generation
  • task1326_qa_zre_question_generation_from_answer
  • task1338_peixian_equity_evaluation_corpus_sentiment_classifier
  • task1729_personachat_generate_next
  • task1202_atomic_classification_xneed
  • task400_paws_paraphrase_classification
  • task502_scruples_anecdotes_whoiswrong_verification
  • task088_identify_typo_verification
  • task221_rocstories_two_choice_classification
  • task200_mnli_entailment_classification
  • task074_squad1.1_question_generation
  • task581_socialiqa_question_generation
  • task1186_nne_hrngo_classification
  • task898_freebase_qa_answer_generation
  • task1408_dart_similarity_classification
  • task168_strategyqa_question_decomposition
  • task1357_xlsum_summary_generation
  • task390_torque_text_span_selection
  • task165_mcscript_question_answering_commonsense
  • task1533_daily_dialog_formal_classification
  • task002_quoref_answer_generation
  • task1297_qasc_question_answering
  • task305_jeopardy_answer_generation_normal
  • task029_winogrande_full_object
  • task1327_qa_zre_answer_generation_from_question
  • task326_jigsaw_classification_obscene
  • task1542_every_ith_element_from_starting
  • task570_recipe_nlg_ner_generation
  • task1409_dart_text_generation
  • task401_numeric_fused_head_reference
  • task846_pubmedqa_classification
  • task1712_poki_classification
  • task344_hybridqa_answer_generation
  • task875_emotion_classification
  • task1214_atomic_classification_xwant
  • task106_scruples_ethical_judgment
  • task238_iirc_answer_from_passage_answer_generation
  • task1391_winogrande_easy_answer_generation
  • task195_sentiment140_classification
  • task163_count_words_ending_with_letter
  • task579_socialiqa_classification
  • task569_recipe_nlg_text_generation
  • task1602_webquestion_question_genreation
  • task747_glucose_cause_emotion_detection
  • task219_rocstories_title_answer_generation
  • task178_quartz_question_answering
  • task103_facts2story_long_text_generation
  • task301_record_question_generation
  • task1369_healthfact_sentence_generation
  • task515_senteval_odd_word_out
  • task496_semeval_answer_generation
  • task1658_billsum_summarization
  • task1204_atomic_classification_hinderedby
  • task1392_superglue_multirc_answer_verification
  • task306_jeopardy_answer_generation_double
  • task1286_openbookqa_question_answering
  • task159_check_frequency_of_words_in_sentence_pair
  • task151_tomqa_find_location_easy_clean
  • task323_jigsaw_classification_sexually_explicit
  • task037_qasc_generate_related_fact
  • task027_drop_answer_type_generation
  • task1596_event2mind_text_generation_2
  • task141_odd-man-out_classification_category
  • task194_duorc_answer_generation
  • task679_hope_edi_english_text_classification
  • task246_dream_question_generation
  • task1195_disflqa_disfluent_to_fluent_conversion
  • task065_timetravel_consistent_sentence_classification
  • task351_winomt_classification_gender_identifiability_anti
  • task580_socialiqa_answer_generation
  • task583_udeps_eng_coarse_pos_tagging
  • task202_mnli_contradiction_classification
  • task222_rocstories_two_chioce_slotting_classification
  • task498_scruples_anecdotes_whoiswrong_classification
  • task067_abductivenli_answer_generation
  • task616_cola_classification
  • task286_olid_offense_judgment
  • task188_snli_neutral_to_entailment_text_modification
  • task223_quartz_explanation_generation
  • task820_protoqa_answer_generation
  • task196_sentiment140_answer_generation
  • task1678_mathqa_answer_selection
  • task349_squad2.0_answerable_unanswerable_question_classification
  • task154_tomqa_find_location_hard_noise
  • task333_hateeval_classification_hate_en
  • task235_iirc_question_from_subtext_answer_generation
  • task1554_scitail_classification
  • task210_logic2text_structured_text_generation
  • task035_winogrande_question_modification_person
  • task230_iirc_passage_classification
  • task1356_xlsum_title_generation
  • task1726_mathqa_correct_answer_generation
  • task302_record_classification
  • task380_boolq_yes_no_question
  • task212_logic2text_classification
  • task748_glucose_reverse_cause_event_detection
  • task834_mathdataset_classification
  • task350_winomt_classification_gender_identifiability_pro
  • task191_hotpotqa_question_generation
  • task236_iirc_question_from_passage_answer_generation
  • task217_rocstories_ordering_answer_generation
  • task568_circa_question_generation
  • task614_glucose_cause_event_detection
  • task361_spolin_yesand_prompt_response_classification
  • task421_persent_sentence_sentiment_classification
  • task203_mnli_sentence_generation
  • task420_persent_document_sentiment_classification
  • task153_tomqa_find_location_hard_clean
  • task346_hybridqa_classification
  • task1211_atomic_classification_hassubevent
  • task360_spolin_yesand_response_generation
  • task510_reddit_tifu_title_summarization
  • task511_reddit_tifu_long_text_summarization
  • task345_hybridqa_answer_generation
  • task270_csrg_counterfactual_context_generation
  • task307_jeopardy_answer_generation_final
  • task001_quoref_question_generation
  • task089_swap_words_verification
  • task1196_atomic_classification_oeffect
  • task080_piqa_answer_generation
  • task1598_nyc_long_text_generation
  • task240_tweetqa_question_generation
  • task615_moviesqa_answer_generation
  • task1347_glue_sts-b_similarity_classification
  • task114_is_the_given_word_longest
  • task292_storycommonsense_character_text_generation
  • task115_help_advice_classification
  • task431_senteval_object_count
  • task1360_numer_sense_multiple_choice_qa_generation
  • task177_para-nmt_paraphrasing
  • task132_dais_text_modification
  • task269_csrg_counterfactual_story_generation
  • task233_iirc_link_exists_classification
  • task161_count_words_containing_letter
  • task1205_atomic_classification_isafter
  • task571_recipe_nlg_ner_generation
  • task1292_yelp_review_full_text_categorization
  • task428_senteval_inversion
  • task311_race_question_generation
  • task429_senteval_tense
  • task403_creak_commonsense_inference
  • task929_products_reviews_classification
  • task582_naturalquestion_answer_generation
  • task237_iirc_answer_from_subtext_answer_generation
  • task050_multirc_answerability
  • task184_break_generate_question
  • task669_ambigqa_answer_generation
  • task169_strategyqa_sentence_generation
  • task500_scruples_anecdotes_title_generation
  • task241_tweetqa_classification
  • task1345_glue_qqp_question_paraprashing
  • task218_rocstories_swap_order_answer_generation
  • task613_politifact_text_generation
  • task1167_penn_treebank_coarse_pos_tagging
  • task1422_mathqa_physics
  • task247_dream_answer_generation
  • task199_mnli_classification
  • task164_mcscript_question_answering_text
  • task1541_agnews_classification
  • task516_senteval_conjoints_inversion
  • task294_storycommonsense_motiv_text_generation
  • task501_scruples_anecdotes_post_type_verification
  • task213_rocstories_correct_ending_classification
  • task821_protoqa_question_generation
  • task493_review_polarity_classification
  • task308_jeopardy_answer_generation_all
  • task1595_event2mind_text_generation_1
  • task040_qasc_question_generation
  • task231_iirc_link_classification
  • task1727_wiqa_what_is_the_effect
  • task578_curiosity_dialogs_answer_generation
  • task310_race_classification
  • task309_race_answer_generation
  • task379_agnews_topic_classification
  • task030_winogrande_full_person
  • task1540_parsed_pdfs_summarization
  • task039_qasc_find_overlapping_words
  • task1206_atomic_classification_isbefore
  • task157_count_vowels_and_consonants
  • task339_record_answer_generation
  • task453_swag_answer_generation
  • task848_pubmedqa_classification
  • task673_google_wellformed_query_classification
  • task676_ollie_relationship_answer_generation
  • task268_casehold_legal_answer_generation
  • task844_financial_phrasebank_classification
  • task330_gap_answer_generation
  • task595_mocha_answer_generation
  • task1285_kpa_keypoint_matching
  • task234_iirc_passage_line_answer_generation
  • task494_review_polarity_answer_generation
  • task670_ambigqa_question_generation
  • task289_gigaword_summarization
  • npr
  • nli
  • SimpleWiki
  • amazon_review_2018
  • ccnews_title_text
  • agnews
  • xsum
  • msmarco
  • yahoo_answers_title_answer
  • squad_pairs
  • wow
  • mteb-amazon_counterfactual-avs_triplets
  • mteb-amazon_massive_intent-avs_triplets
  • mteb-amazon_massive_scenario-avs_triplets
  • mteb-amazon_reviews_multi-avs_triplets
  • mteb-banking77-avs_triplets
  • mteb-emotion-avs_triplets
  • mteb-imdb-avs_triplets
  • mteb-mtop_domain-avs_triplets
  • mteb-mtop_intent-avs_triplets
  • mteb-toxic_conversations_50k-avs_triplets
  • mteb-tweet_sentiment_extraction-avs_triplets
  • covid-bing-query-gpt4-avs_triplets
• Language: en
• License: apache-2.0

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): RandomProjection({'in_features': 384, 'out_features': 4096, 'seed': 42, 'requires_grad': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("avsolatorio/all-MiniLM-L6-v2-MEDI-MTEB-triplet-randproj-trainableParams-GIST-32-best")
# Run inference
sentences = [
    'Does risk for anxiety increase during the menopausal transition?',
    "Little is known about the risk of anxiety in women during midlife and the menopausal transition. We examined anxiety as a cluster of four symptoms and determined the association between menopausal stage and high anxiety during 10 years of follow-up of 2,956 women of multiple race/ethnicities.This study was a longitudinal analysis of data from the multisite Study of Women's Health Across the Nation, a study of menopause and aging. Women were aged 42 to 52 at study entry. The outcome was high anxiety, with a score of 4 or greater on the sum of four anxiety symptoms rated according to frequency in the previous 2 weeks from 0 (none) to 4 (daily; upper 20%). Covariates included sociodemographics, health factors, stressors, and vasomotor symptoms.Women with low anxiety at baseline were more likely to report high-anxiety symptoms at early or late perimenopause or postmenopause than at premenopause (odds ratio, 1.56-1.61), independent of multiple risk factors, including upsetting life events, financial strain, fair/poor perceived health, and vasomotor symptoms. Women with high anxiety at baseline continued to have high rates of high anxiety throughout follow-up, but odds ratios did not differ by menopausal stage.",
    "Small 14F pigtail catheters (PCs) have been shown to drain air quite well in patients with traumatic pneumothorax (PTX). But their effectiveness in draining blood in patients with traumatic hemothorax (HTX) or hemopneumothorax (HPTX) is unknown. We hypothesized that 14F PCs can drain blood as well as large-bore 32F to 40F chest tubes. We herein report our early case series experience with PCs in the management of traumatic HTX and HPTX.We prospectively collected data on all bedside-inserted PCs in patients with traumatic HTX or HPTX during a 30-month period (July 2009 through December 2011) at our Level I trauma center. We then compared our PC prospective data with our trauma registry-derived retrospective chest tube data (January 2008 through December 2010) at our center. Our primary outcome of interest was the initial drainage output. Our secondary outcomes were tube duration, insertion-related complications, and failure rate. For our statistical analysis, we used the unpaired Student's t-test, χ test, and Wilcoxon rank-sum test; we defined significance by a value of p<0.05.A total of 36 patients received PCs, and 191 received chest tubes. Our PC group had a higher rate of blunt mechanism injuries than our chest tube group did (83 vs. 62%; p = 0.01). The mean initial output was similar between our PC group (560 ± 81 mL) and our chest tube group (426 ± 37 mL) (p = 0.13). In the PC group, the tube was inserted later (median, Day 1; interquartile range, Days 0-3) than the tube inserted in our chest tube group (median, Day 0; interquartile range, Days 0-0) (p<0.001). Tube duration, rate of insertion-related complications, and failure rate were all similar.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 4096]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Triplet

• Dataset: medi-mteb-dev
• Evaluated with TripletEvaluator

Training Details

Training Datasets

NQ

• Dataset: NQ
• Size: 49,516 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 10 tokens mean: 11.81 tokens max: 29 tokens	min: 112 tokens mean: 138.06 tokens max: 256 tokens	min: 110 tokens mean: 138.69 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

pubmed

• Dataset: pubmed
• Size: 29,716 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 22.69 tokens max: 56 tokens	min: 96 tokens mean: 240.09 tokens max: 256 tokens	min: 57 tokens mean: 239.66 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

specter_train_triples

• Dataset: specter_train_triples
• Size: 49,516 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 15.08 tokens max: 41 tokens	min: 4 tokens mean: 13.77 tokens max: 43 tokens	min: 5 tokens mean: 15.98 tokens max: 70 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

S2ORC_citations_abstracts

• Dataset: S2ORC_citations_abstracts
• Size: 99,000 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 16 tokens mean: 198.27 tokens max: 256 tokens	min: 21 tokens mean: 202.73 tokens max: 256 tokens	min: 23 tokens mean: 204.28 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

fever

• Dataset: fever
• Size: 74,258 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 12.47 tokens max: 34 tokens	min: 48 tokens mean: 112.39 tokens max: 154 tokens	min: 44 tokens mean: 114.2 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

gooaq_pairs

• Dataset: gooaq_pairs
• Size: 24,774 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 8 tokens mean: 11.85 tokens max: 26 tokens	min: 15 tokens mean: 60.13 tokens max: 147 tokens	min: 16 tokens mean: 63.34 tokens max: 154 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

codesearchnet

• Dataset: codesearchnet
• Size: 14,858 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 28.74 tokens max: 156 tokens	min: 28 tokens mean: 132.73 tokens max: 256 tokens	min: 27 tokens mean: 159.52 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

wikihow

• Dataset: wikihow
• Size: 4,974 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 8.13 tokens max: 21 tokens	min: 11 tokens mean: 45.3 tokens max: 90 tokens	min: 8 tokens mean: 36.34 tokens max: 100 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

WikiAnswers

• Dataset: WikiAnswers
• Size: 24,774 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 12.92 tokens max: 41 tokens	min: 6 tokens mean: 12.97 tokens max: 47 tokens	min: 6 tokens mean: 13.13 tokens max: 33 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

eli5_question_answer

• Dataset: eli5_question_answer
• Size: 24,774 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 20.48 tokens max: 70 tokens	min: 13 tokens mean: 98.39 tokens max: 256 tokens	min: 15 tokens mean: 110.41 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

amazon-qa

• Dataset: amazon-qa
• Size: 99,000 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 22.87 tokens max: 256 tokens	min: 17 tokens mean: 53.28 tokens max: 256 tokens	min: 16 tokens mean: 60.75 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

medmcqa

• Dataset: medmcqa
• Size: 29,716 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 21.72 tokens max: 256 tokens	min: 3 tokens mean: 113.14 tokens max: 256 tokens	min: 3 tokens mean: 106.32 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

zeroshot

• Dataset: zeroshot
• Size: 14,858 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 8.62 tokens max: 20 tokens	min: 15 tokens mean: 112.27 tokens max: 155 tokens	min: 16 tokens mean: 117.5 tokens max: 196 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

TriviaQA_pairs

• Dataset: TriviaQA_pairs
• Size: 49,516 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 8 tokens mean: 19.92 tokens max: 192 tokens	min: 13 tokens mean: 245.35 tokens max: 256 tokens	min: 21 tokens mean: 232.81 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

PAQ_pairs

• Dataset: PAQ_pairs
• Size: 24,774 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 12.62 tokens max: 20 tokens	min: 107 tokens mean: 136.58 tokens max: 210 tokens	min: 112 tokens mean: 136.02 tokens max: 254 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

stackexchange_duplicate_questions_title-body_title-body

• Dataset: stackexchange_duplicate_questions_title-body_title-body
• Size: 24,774 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 17 tokens mean: 145.53 tokens max: 256 tokens	min: 19 tokens mean: 141.81 tokens max: 256 tokens	min: 26 tokens mean: 200.11 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

trex

• Dataset: trex
• Size: 29,716 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 9.67 tokens max: 27 tokens	min: 19 tokens mean: 104.98 tokens max: 158 tokens	min: 16 tokens mean: 117.08 tokens max: 219 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

flickr30k_captions

• Dataset: flickr30k_captions
• Size: 24,774 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 16.1 tokens max: 56 tokens	min: 7 tokens mean: 16.21 tokens max: 64 tokens	min: 7 tokens mean: 17.38 tokens max: 61 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

hotpotqa

• Dataset: hotpotqa
• Size: 39,600 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 8 tokens mean: 24.26 tokens max: 110 tokens	min: 9 tokens mean: 113.27 tokens max: 155 tokens	min: 48 tokens mean: 115.28 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task671_ambigqa_text_generation

• Dataset: task671_ambigqa_text_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 11 tokens mean: 12.69 tokens max: 26 tokens	min: 11 tokens mean: 12.48 tokens max: 23 tokens	min: 11 tokens mean: 12.22 tokens max: 19 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task061_ropes_answer_generation

• Dataset: task061_ropes_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 117 tokens mean: 209.99 tokens max: 256 tokens	min: 117 tokens mean: 209.3 tokens max: 256 tokens	min: 119 tokens mean: 211.61 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task285_imdb_answer_generation

• Dataset: task285_imdb_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 46 tokens mean: 209.37 tokens max: 256 tokens	min: 49 tokens mean: 204.38 tokens max: 256 tokens	min: 46 tokens mean: 209.37 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task905_hate_speech_offensive_classification

• Dataset: task905_hate_speech_offensive_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 15 tokens mean: 41.29 tokens max: 164 tokens	min: 13 tokens mean: 40.39 tokens max: 198 tokens	min: 13 tokens mean: 32.04 tokens max: 135 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task566_circa_classification

• Dataset: task566_circa_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 20 tokens mean: 27.76 tokens max: 48 tokens	min: 19 tokens mean: 27.26 tokens max: 44 tokens	min: 20 tokens mean: 27.48 tokens max: 47 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task184_snli_entailment_to_neutral_text_modification

• Dataset: task184_snli_entailment_to_neutral_text_modification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 17 tokens mean: 29.87 tokens max: 72 tokens	min: 16 tokens mean: 28.87 tokens max: 60 tokens	min: 17 tokens mean: 30.19 tokens max: 100 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task280_stereoset_classification_stereotype_type

• Dataset: task280_stereoset_classification_stereotype_type
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 8 tokens mean: 18.53 tokens max: 53 tokens	min: 8 tokens mean: 16.82 tokens max: 53 tokens	min: 8 tokens mean: 16.81 tokens max: 51 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1599_smcalflow_classification

• Dataset: task1599_smcalflow_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 3 tokens mean: 11.23 tokens max: 37 tokens	min: 3 tokens mean: 10.46 tokens max: 38 tokens	min: 5 tokens mean: 16.23 tokens max: 45 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1384_deal_or_no_dialog_classification

• Dataset: task1384_deal_or_no_dialog_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 14 tokens mean: 59.0 tokens max: 256 tokens	min: 12 tokens mean: 59.08 tokens max: 256 tokens	min: 15 tokens mean: 58.49 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task591_sciq_answer_generation

• Dataset: task591_sciq_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 8 tokens mean: 17.55 tokens max: 70 tokens	min: 7 tokens mean: 17.17 tokens max: 43 tokens	min: 6 tokens mean: 16.69 tokens max: 75 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task823_peixian-rtgender_sentiment_analysis

• Dataset: task823_peixian-rtgender_sentiment_analysis
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 16 tokens mean: 57.25 tokens max: 179 tokens	min: 16 tokens mean: 59.79 tokens max: 153 tokens	min: 14 tokens mean: 60.91 tokens max: 169 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task023_cosmosqa_question_generation

• Dataset: task023_cosmosqa_question_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 35 tokens mean: 79.3 tokens max: 159 tokens	min: 35 tokens mean: 79.94 tokens max: 165 tokens	min: 35 tokens mean: 79.61 tokens max: 161 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task900_freebase_qa_category_classification

• Dataset: task900_freebase_qa_category_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 8 tokens mean: 20.49 tokens max: 88 tokens	min: 8 tokens mean: 18.37 tokens max: 62 tokens	min: 8 tokens mean: 19.19 tokens max: 69 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task924_event2mind_word_generation

• Dataset: task924_event2mind_word_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 17 tokens mean: 32.16 tokens max: 64 tokens	min: 17 tokens mean: 32.11 tokens max: 70 tokens	min: 17 tokens mean: 31.4 tokens max: 68 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task152_tomqa_find_location_easy_noise

• Dataset: task152_tomqa_find_location_easy_noise
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 37 tokens mean: 52.89 tokens max: 79 tokens	min: 37 tokens mean: 52.47 tokens max: 78 tokens	min: 37 tokens mean: 52.77 tokens max: 82 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1368_healthfact_sentence_generation

• Dataset: task1368_healthfact_sentence_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 91 tokens mean: 240.56 tokens max: 256 tokens	min: 84 tokens mean: 240.0 tokens max: 256 tokens	min: 97 tokens mean: 245.91 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1661_super_glue_classification

• Dataset: task1661_super_glue_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 35 tokens mean: 141.05 tokens max: 256 tokens	min: 31 tokens mean: 143.19 tokens max: 256 tokens	min: 31 tokens mean: 143.02 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1187_politifact_classification

• Dataset: task1187_politifact_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 14 tokens mean: 33.25 tokens max: 79 tokens	min: 10 tokens mean: 31.33 tokens max: 75 tokens	min: 13 tokens mean: 31.85 tokens max: 71 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1728_web_nlg_data_to_text

• Dataset: task1728_web_nlg_data_to_text
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 42.76 tokens max: 152 tokens	min: 7 tokens mean: 46.36 tokens max: 152 tokens	min: 8 tokens mean: 42.84 tokens max: 152 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task112_asset_simple_sentence_identification

• Dataset: task112_asset_simple_sentence_identification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 18 tokens mean: 52.02 tokens max: 136 tokens	min: 18 tokens mean: 51.9 tokens max: 144 tokens	min: 22 tokens mean: 52.05 tokens max: 114 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1340_msr_text_compression_compression

• Dataset: task1340_msr_text_compression_compression
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 14 tokens mean: 42.05 tokens max: 116 tokens	min: 14 tokens mean: 44.21 tokens max: 133 tokens	min: 12 tokens mean: 39.89 tokens max: 141 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task072_abductivenli_answer_generation

• Dataset: task072_abductivenli_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 17 tokens mean: 26.86 tokens max: 56 tokens	min: 16 tokens mean: 26.18 tokens max: 47 tokens	min: 16 tokens mean: 26.42 tokens max: 55 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1504_hatexplain_answer_generation

• Dataset: task1504_hatexplain_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 28.71 tokens max: 72 tokens	min: 5 tokens mean: 24.49 tokens max: 86 tokens	min: 5 tokens mean: 27.64 tokens max: 67 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task684_online_privacy_policy_text_information_type_generation

• Dataset: task684_online_privacy_policy_text_information_type_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 10 tokens mean: 29.91 tokens max: 68 tokens	min: 10 tokens mean: 30.17 tokens max: 61 tokens	min: 14 tokens mean: 30.06 tokens max: 68 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1290_xsum_summarization

• Dataset: task1290_xsum_summarization
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 39 tokens mean: 226.11 tokens max: 256 tokens	min: 50 tokens mean: 229.39 tokens max: 256 tokens	min: 34 tokens mean: 230.44 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task075_squad1.1_answer_generation

• Dataset: task075_squad1.1_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 48 tokens mean: 168.02 tokens max: 256 tokens	min: 45 tokens mean: 173.03 tokens max: 256 tokens	min: 46 tokens mean: 180.7 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1587_scifact_classification

• Dataset: task1587_scifact_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 88 tokens mean: 242.44 tokens max: 256 tokens	min: 90 tokens mean: 246.54 tokens max: 256 tokens	min: 86 tokens mean: 245.23 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task384_socialiqa_question_classification

• Dataset: task384_socialiqa_question_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 24 tokens mean: 35.4 tokens max: 78 tokens	min: 22 tokens mean: 34.4 tokens max: 59 tokens	min: 22 tokens mean: 34.59 tokens max: 57 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1555_scitail_answer_generation

• Dataset: task1555_scitail_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 18 tokens mean: 36.93 tokens max: 90 tokens	min: 18 tokens mean: 36.4 tokens max: 80 tokens	min: 18 tokens mean: 36.82 tokens max: 92 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1532_daily_dialog_emotion_classification

• Dataset: task1532_daily_dialog_emotion_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 16 tokens mean: 136.19 tokens max: 256 tokens	min: 15 tokens mean: 140.45 tokens max: 256 tokens	min: 17 tokens mean: 134.23 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task239_tweetqa_answer_generation

• Dataset: task239_tweetqa_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 28 tokens mean: 55.95 tokens max: 91 tokens	min: 29 tokens mean: 56.47 tokens max: 92 tokens	min: 25 tokens mean: 56.04 tokens max: 81 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task596_mocha_question_generation

• Dataset: task596_mocha_question_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 34 tokens mean: 80.5 tokens max: 163 tokens	min: 12 tokens mean: 95.51 tokens max: 256 tokens	min: 10 tokens mean: 45.22 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1411_dart_subject_identification

• Dataset: task1411_dart_subject_identification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 14.93 tokens max: 74 tokens	min: 6 tokens mean: 14.09 tokens max: 37 tokens	min: 6 tokens mean: 14.31 tokens max: 38 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1359_numer_sense_answer_generation

• Dataset: task1359_numer_sense_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 10 tokens mean: 18.72 tokens max: 30 tokens	min: 10 tokens mean: 18.42 tokens max: 33 tokens	min: 10 tokens mean: 18.37 tokens max: 30 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task329_gap_classification

• Dataset: task329_gap_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 40 tokens mean: 123.89 tokens max: 256 tokens	min: 62 tokens mean: 127.01 tokens max: 256 tokens	min: 58 tokens mean: 128.77 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task220_rocstories_title_classification

• Dataset: task220_rocstories_title_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 53 tokens mean: 80.82 tokens max: 116 tokens	min: 51 tokens mean: 81.14 tokens max: 108 tokens	min: 55 tokens mean: 79.9 tokens max: 115 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task316_crows-pairs_classification_stereotype

• Dataset: task316_crows-pairs_classification_stereotype
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 8 tokens mean: 19.75 tokens max: 51 tokens	min: 7 tokens mean: 18.19 tokens max: 41 tokens	min: 7 tokens mean: 19.79 tokens max: 52 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task495_semeval_headline_classification

• Dataset: task495_semeval_headline_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 17 tokens mean: 24.53 tokens max: 42 tokens	min: 15 tokens mean: 24.18 tokens max: 41 tokens	min: 15 tokens mean: 24.15 tokens max: 38 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1168_brown_coarse_pos_tagging

• Dataset: task1168_brown_coarse_pos_tagging
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 13 tokens mean: 43.7 tokens max: 142 tokens	min: 13 tokens mean: 43.14 tokens max: 197 tokens	min: 12 tokens mean: 44.66 tokens max: 197 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task348_squad2.0_unanswerable_question_generation

• Dataset: task348_squad2.0_unanswerable_question_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 30 tokens mean: 152.78 tokens max: 256 tokens	min: 38 tokens mean: 160.71 tokens max: 256 tokens	min: 33 tokens mean: 165.2 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task049_multirc_questions_needed_to_answer

• Dataset: task049_multirc_questions_needed_to_answer
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 174 tokens mean: 252.69 tokens max: 256 tokens	min: 169 tokens mean: 252.79 tokens max: 256 tokens	min: 178 tokens mean: 252.89 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1534_daily_dialog_question_classification

• Dataset: task1534_daily_dialog_question_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 17 tokens mean: 125.75 tokens max: 256 tokens	min: 15 tokens mean: 131.36 tokens max: 256 tokens	min: 16 tokens mean: 136.17 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task322_jigsaw_classification_threat

• Dataset: task322_jigsaw_classification_threat
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 54.31 tokens max: 256 tokens	min: 6 tokens mean: 61.66 tokens max: 249 tokens	min: 6 tokens mean: 61.93 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task295_semeval_2020_task4_commonsense_reasoning

• Dataset: task295_semeval_2020_task4_commonsense_reasoning
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 25 tokens mean: 45.27 tokens max: 92 tokens	min: 25 tokens mean: 45.05 tokens max: 95 tokens	min: 25 tokens mean: 44.61 tokens max: 88 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task186_snli_contradiction_to_entailment_text_modification

• Dataset: task186_snli_contradiction_to_entailment_text_modification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 18 tokens mean: 31.04 tokens max: 102 tokens	min: 18 tokens mean: 30.19 tokens max: 65 tokens	min: 18 tokens mean: 32.19 tokens max: 67 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task034_winogrande_question_modification_object

• Dataset: task034_winogrande_question_modification_object
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 29 tokens mean: 36.28 tokens max: 53 tokens	min: 29 tokens mean: 35.62 tokens max: 54 tokens	min: 29 tokens mean: 34.83 tokens max: 55 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task160_replace_letter_in_a_sentence

• Dataset: task160_replace_letter_in_a_sentence
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 29 tokens mean: 31.98 tokens max: 49 tokens	min: 28 tokens mean: 31.75 tokens max: 41 tokens	min: 29 tokens mean: 31.8 tokens max: 48 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task469_mrqa_answer_generation

• Dataset: task469_mrqa_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 27 tokens mean: 183.47 tokens max: 256 tokens	min: 25 tokens mean: 182.09 tokens max: 256 tokens	min: 27 tokens mean: 185.25 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task105_story_cloze-rocstories_sentence_generation

• Dataset: task105_story_cloze-rocstories_sentence_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 36 tokens mean: 55.7 tokens max: 75 tokens	min: 35 tokens mean: 54.94 tokens max: 76 tokens	min: 36 tokens mean: 55.9 tokens max: 76 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task649_race_blank_question_generation

• Dataset: task649_race_blank_question_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 36 tokens mean: 253.05 tokens max: 256 tokens	min: 36 tokens mean: 252.87 tokens max: 256 tokens	min: 157 tokens mean: 253.87 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1536_daily_dialog_happiness_classification

• Dataset: task1536_daily_dialog_happiness_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 13 tokens mean: 127.57 tokens max: 256 tokens	min: 13 tokens mean: 135.04 tokens max: 256 tokens	min: 16 tokens mean: 143.06 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task683_online_privacy_policy_text_purpose_answer_generation

• Dataset: task683_online_privacy_policy_text_purpose_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 10 tokens mean: 29.97 tokens max: 68 tokens	min: 10 tokens mean: 30.32 tokens max: 64 tokens	min: 14 tokens mean: 29.94 tokens max: 68 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task024_cosmosqa_answer_generation

• Dataset: task024_cosmosqa_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 45 tokens mean: 92.55 tokens max: 176 tokens	min: 47 tokens mean: 93.52 tokens max: 174 tokens	min: 42 tokens mean: 94.85 tokens max: 183 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task584_udeps_eng_fine_pos_tagging

• Dataset: task584_udeps_eng_fine_pos_tagging
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 12 tokens mean: 40.17 tokens max: 120 tokens	min: 12 tokens mean: 39.59 tokens max: 186 tokens	min: 12 tokens mean: 40.53 tokens max: 148 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task066_timetravel_binary_consistency_classification

• Dataset: task066_timetravel_binary_consistency_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 42 tokens mean: 66.82 tokens max: 93 tokens	min: 43 tokens mean: 67.45 tokens max: 94 tokens	min: 45 tokens mean: 67.22 tokens max: 92 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task413_mickey_en_sentence_perturbation_generation

• Dataset: task413_mickey_en_sentence_perturbation_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 13.73 tokens max: 21 tokens	min: 7 tokens mean: 13.77 tokens max: 21 tokens	min: 7 tokens mean: 13.29 tokens max: 20 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task182_duorc_question_generation

• Dataset: task182_duorc_question_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 99 tokens mean: 242.82 tokens max: 256 tokens	min: 120 tokens mean: 246.52 tokens max: 256 tokens	min: 99 tokens mean: 246.22 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task028_drop_answer_generation

• Dataset: task028_drop_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 76 tokens mean: 231.19 tokens max: 256 tokens	min: 86 tokens mean: 234.73 tokens max: 256 tokens	min: 81 tokens mean: 235.92 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1601_webquestions_answer_generation

• Dataset: task1601_webquestions_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 9 tokens mean: 16.52 tokens max: 28 tokens	min: 11 tokens mean: 16.7 tokens max: 28 tokens	min: 9 tokens mean: 16.78 tokens max: 27 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1295_adversarial_qa_question_answering

• Dataset: task1295_adversarial_qa_question_answering
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 45 tokens mean: 165.27 tokens max: 256 tokens	min: 54 tokens mean: 166.85 tokens max: 256 tokens	min: 48 tokens mean: 167.46 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task201_mnli_neutral_classification

• Dataset: task201_mnli_neutral_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 24 tokens mean: 72.94 tokens max: 218 tokens	min: 25 tokens mean: 73.25 tokens max: 170 tokens	min: 27 tokens mean: 72.58 tokens max: 205 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task038_qasc_combined_fact

• Dataset: task038_qasc_combined_fact
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 18 tokens mean: 31.26 tokens max: 57 tokens	min: 19 tokens mean: 30.52 tokens max: 53 tokens	min: 18 tokens mean: 30.77 tokens max: 53 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task293_storycommonsense_emotion_text_generation

• Dataset: task293_storycommonsense_emotion_text_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 14 tokens mean: 40.15 tokens max: 86 tokens	min: 15 tokens mean: 40.08 tokens max: 86 tokens	min: 14 tokens mean: 37.9 tokens max: 86 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task572_recipe_nlg_text_generation

• Dataset: task572_recipe_nlg_text_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 24 tokens mean: 114.66 tokens max: 256 tokens	min: 24 tokens mean: 121.4 tokens max: 256 tokens	min: 24 tokens mean: 123.56 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task517_emo_classify_emotion_of_dialogue

• Dataset: task517_emo_classify_emotion_of_dialogue
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 18.08 tokens max: 78 tokens	min: 7 tokens mean: 16.95 tokens max: 59 tokens	min: 7 tokens mean: 18.49 tokens max: 67 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task382_hybridqa_answer_generation

• Dataset: task382_hybridqa_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 29 tokens mean: 42.18 tokens max: 70 tokens	min: 29 tokens mean: 41.63 tokens max: 74 tokens	min: 28 tokens mean: 41.67 tokens max: 75 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task176_break_decompose_questions

• Dataset: task176_break_decompose_questions
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 9 tokens mean: 17.48 tokens max: 41 tokens	min: 8 tokens mean: 17.23 tokens max: 39 tokens	min: 8 tokens mean: 15.66 tokens max: 38 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1291_multi_news_summarization

• Dataset: task1291_multi_news_summarization
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 116 tokens mean: 255.47 tokens max: 256 tokens	min: 146 tokens mean: 255.58 tokens max: 256 tokens	min: 68 tokens mean: 251.14 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task155_count_nouns_verbs

• Dataset: task155_count_nouns_verbs
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 23 tokens mean: 27.0 tokens max: 56 tokens	min: 23 tokens mean: 26.8 tokens max: 43 tokens	min: 23 tokens mean: 26.94 tokens max: 46 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task031_winogrande_question_generation_object

• Dataset: task031_winogrande_question_generation_object
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 7.43 tokens max: 11 tokens	min: 7 tokens mean: 7.31 tokens max: 11 tokens	min: 7 tokens mean: 7.27 tokens max: 11 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task279_stereoset_classification_stereotype

• Dataset: task279_stereoset_classification_stereotype
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 8 tokens mean: 17.89 tokens max: 41 tokens	min: 8 tokens mean: 15.47 tokens max: 43 tokens	min: 8 tokens mean: 17.31 tokens max: 50 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1336_peixian_equity_evaluation_corpus_gender_classifier

• Dataset: task1336_peixian_equity_evaluation_corpus_gender_classifier
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 9.62 tokens max: 17 tokens	min: 6 tokens mean: 9.59 tokens max: 16 tokens	min: 6 tokens mean: 9.68 tokens max: 16 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task508_scruples_dilemmas_more_ethical_isidentifiable

• Dataset: task508_scruples_dilemmas_more_ethical_isidentifiable
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 12 tokens mean: 29.71 tokens max: 94 tokens	min: 12 tokens mean: 28.53 tokens max: 94 tokens	min: 12 tokens mean: 28.67 tokens max: 86 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task518_emo_different_dialogue_emotions

• Dataset: task518_emo_different_dialogue_emotions
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 28 tokens mean: 47.65 tokens max: 106 tokens	min: 28 tokens mean: 45.38 tokens max: 116 tokens	min: 26 tokens mean: 45.93 tokens max: 123 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task077_splash_explanation_to_sql

• Dataset: task077_splash_explanation_to_sql
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 8 tokens mean: 40.0 tokens max: 126 tokens	min: 8 tokens mean: 39.96 tokens max: 126 tokens	min: 8 tokens mean: 35.92 tokens max: 111 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task923_event2mind_classifier

• Dataset: task923_event2mind_classifier
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 10 tokens mean: 20.61 tokens max: 46 tokens	min: 11 tokens mean: 18.68 tokens max: 41 tokens	min: 11 tokens mean: 19.64 tokens max: 46 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task470_mrqa_question_generation

• Dataset: task470_mrqa_question_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 13 tokens mean: 173.16 tokens max: 256 tokens	min: 11 tokens mean: 175.77 tokens max: 256 tokens	min: 14 tokens mean: 180.88 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task638_multi_woz_classification

• Dataset: task638_multi_woz_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 78 tokens mean: 223.14 tokens max: 256 tokens	min: 76 tokens mean: 220.01 tokens max: 256 tokens	min: 64 tokens mean: 219.88 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1412_web_questions_question_answering

• Dataset: task1412_web_questions_question_answering
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 10.34 tokens max: 17 tokens	min: 6 tokens mean: 10.22 tokens max: 17 tokens	min: 6 tokens mean: 10.1 tokens max: 16 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task847_pubmedqa_question_generation

• Dataset: task847_pubmedqa_question_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 21 tokens mean: 248.9 tokens max: 256 tokens	min: 21 tokens mean: 248.66 tokens max: 256 tokens	min: 43 tokens mean: 248.65 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task678_ollie_actual_relationship_answer_generation

• Dataset: task678_ollie_actual_relationship_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 20 tokens mean: 40.7 tokens max: 95 tokens	min: 19 tokens mean: 38.18 tokens max: 102 tokens	min: 18 tokens mean: 41.24 tokens max: 104 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task290_tellmewhy_question_answerability

• Dataset: task290_tellmewhy_question_answerability
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 37 tokens mean: 62.78 tokens max: 95 tokens	min: 36 tokens mean: 62.33 tokens max: 94 tokens	min: 37 tokens mean: 62.88 tokens max: 95 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task575_air_dialogue_classification

• Dataset: task575_air_dialogue_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 14.1 tokens max: 45 tokens	min: 4 tokens mean: 13.46 tokens max: 43 tokens	min: 4 tokens mean: 12.22 tokens max: 42 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task189_snli_neutral_to_contradiction_text_modification

• Dataset: task189_snli_neutral_to_contradiction_text_modification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 18 tokens mean: 31.88 tokens max: 60 tokens	min: 18 tokens mean: 30.77 tokens max: 57 tokens	min: 18 tokens mean: 33.23 tokens max: 105 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task026_drop_question_generation

• Dataset: task026_drop_question_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 82 tokens mean: 219.21 tokens max: 256 tokens	min: 57 tokens mean: 222.44 tokens max: 256 tokens	min: 96 tokens mean: 232.43 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task162_count_words_starting_with_letter

• Dataset: task162_count_words_starting_with_letter
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 28 tokens mean: 32.17 tokens max: 56 tokens	min: 28 tokens mean: 31.77 tokens max: 45 tokens	min: 28 tokens mean: 31.63 tokens max: 46 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task079_conala_concat_strings

• Dataset: task079_conala_concat_strings
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 11 tokens mean: 39.85 tokens max: 76 tokens	min: 11 tokens mean: 34.41 tokens max: 80 tokens	min: 11 tokens mean: 33.8 tokens max: 76 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task610_conllpp_ner

• Dataset: task610_conllpp_ner
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 19.53 tokens max: 62 tokens	min: 4 tokens mean: 20.38 tokens max: 62 tokens	min: 4 tokens mean: 14.27 tokens max: 54 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task046_miscellaneous_question_typing

• Dataset: task046_miscellaneous_question_typing
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 16 tokens mean: 25.35 tokens max: 70 tokens	min: 16 tokens mean: 24.83 tokens max: 70 tokens	min: 16 tokens mean: 25.09 tokens max: 57 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task197_mnli_domain_answer_generation

• Dataset: task197_mnli_domain_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 15 tokens mean: 44.05 tokens max: 197 tokens	min: 12 tokens mean: 44.84 tokens max: 211 tokens	min: 11 tokens mean: 39.29 tokens max: 115 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1325_qa_zre_question_generation_on_subject_relation

• Dataset: task1325_qa_zre_question_generation_on_subject_relation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 18 tokens mean: 50.66 tokens max: 256 tokens	min: 20 tokens mean: 49.36 tokens max: 180 tokens	min: 22 tokens mean: 54.57 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task430_senteval_subject_count

• Dataset: task430_senteval_subject_count
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 17.32 tokens max: 35 tokens	min: 7 tokens mean: 15.38 tokens max: 34 tokens	min: 7 tokens mean: 16.04 tokens max: 34 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task672_nummersense

• Dataset: task672_nummersense
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 15.67 tokens max: 30 tokens	min: 7 tokens mean: 15.35 tokens max: 27 tokens	min: 7 tokens mean: 15.28 tokens max: 30 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task402_grailqa_paraphrase_generation

• Dataset: task402_grailqa_paraphrase_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 23 tokens mean: 131.23 tokens max: 256 tokens	min: 24 tokens mean: 140.42 tokens max: 256 tokens	min: 22 tokens mean: 138.52 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task904_hate_speech_offensive_classification

• Dataset: task904_hate_speech_offensive_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 8 tokens mean: 34.18 tokens max: 157 tokens	min: 8 tokens mean: 33.96 tokens max: 256 tokens	min: 5 tokens mean: 27.72 tokens max: 148 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task192_hotpotqa_sentence_generation

• Dataset: task192_hotpotqa_sentence_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 37 tokens mean: 125.2 tokens max: 256 tokens	min: 35 tokens mean: 123.63 tokens max: 256 tokens	min: 33 tokens mean: 133.51 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task069_abductivenli_classification

• Dataset: task069_abductivenli_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 33 tokens mean: 52.03 tokens max: 86 tokens	min: 33 tokens mean: 51.95 tokens max: 95 tokens	min: 33 tokens mean: 51.93 tokens max: 95 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task574_air_dialogue_sentence_generation

• Dataset: task574_air_dialogue_sentence_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 54 tokens mean: 144.29 tokens max: 256 tokens	min: 57 tokens mean: 143.72 tokens max: 256 tokens	min: 66 tokens mean: 148.06 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task187_snli_entailment_to_contradiction_text_modification

• Dataset: task187_snli_entailment_to_contradiction_text_modification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 16 tokens mean: 30.2 tokens max: 69 tokens	min: 16 tokens mean: 29.88 tokens max: 104 tokens	min: 17 tokens mean: 29.38 tokens max: 71 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task749_glucose_reverse_cause_emotion_detection

• Dataset: task749_glucose_reverse_cause_emotion_detection
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 38 tokens mean: 67.68 tokens max: 106 tokens	min: 37 tokens mean: 67.11 tokens max: 104 tokens	min: 39 tokens mean: 68.67 tokens max: 107 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1552_scitail_question_generation

• Dataset: task1552_scitail_question_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 18.32 tokens max: 53 tokens	min: 7 tokens mean: 17.47 tokens max: 46 tokens	min: 7 tokens mean: 15.91 tokens max: 54 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task750_aqua_multiple_choice_answering

• Dataset: task750_aqua_multiple_choice_answering
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 33 tokens mean: 70.16 tokens max: 194 tokens	min: 32 tokens mean: 68.57 tokens max: 194 tokens	min: 28 tokens mean: 68.39 tokens max: 165 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task327_jigsaw_classification_toxic

• Dataset: task327_jigsaw_classification_toxic
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 36.92 tokens max: 234 tokens	min: 5 tokens mean: 41.25 tokens max: 256 tokens	min: 5 tokens mean: 45.5 tokens max: 244 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1502_hatexplain_classification

• Dataset: task1502_hatexplain_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 28.96 tokens max: 73 tokens	min: 5 tokens mean: 27.0 tokens max: 110 tokens	min: 5 tokens mean: 27.14 tokens max: 90 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task328_jigsaw_classification_insult

• Dataset: task328_jigsaw_classification_insult
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 52.01 tokens max: 247 tokens	min: 5 tokens mean: 62.29 tokens max: 256 tokens	min: 5 tokens mean: 65.49 tokens max: 249 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task304_numeric_fused_head_resolution

• Dataset: task304_numeric_fused_head_resolution
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 15 tokens mean: 118.81 tokens max: 256 tokens	min: 12 tokens mean: 120.02 tokens max: 256 tokens	min: 11 tokens mean: 133.42 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1293_kilt_tasks_hotpotqa_question_answering

• Dataset: task1293_kilt_tasks_hotpotqa_question_answering
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 10 tokens mean: 24.76 tokens max: 114 tokens	min: 9 tokens mean: 24.22 tokens max: 114 tokens	min: 8 tokens mean: 23.8 tokens max: 84 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task216_rocstories_correct_answer_generation

• Dataset: task216_rocstories_correct_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 39 tokens mean: 59.42 tokens max: 83 tokens	min: 36 tokens mean: 58.26 tokens max: 92 tokens	min: 39 tokens mean: 58.24 tokens max: 95 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1326_qa_zre_question_generation_from_answer

• Dataset: task1326_qa_zre_question_generation_from_answer
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 17 tokens mean: 46.43 tokens max: 256 tokens	min: 14 tokens mean: 45.21 tokens max: 256 tokens	min: 18 tokens mean: 49.25 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1338_peixian_equity_evaluation_corpus_sentiment_classifier

• Dataset: task1338_peixian_equity_evaluation_corpus_sentiment_classifier
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 9.66 tokens max: 16 tokens	min: 6 tokens mean: 9.68 tokens max: 16 tokens	min: 6 tokens mean: 9.55 tokens max: 17 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1729_personachat_generate_next

• Dataset: task1729_personachat_generate_next
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 44 tokens mean: 146.11 tokens max: 256 tokens	min: 43 tokens mean: 141.79 tokens max: 256 tokens	min: 50 tokens mean: 143.69 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1202_atomic_classification_xneed

• Dataset: task1202_atomic_classification_xneed
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 14 tokens mean: 19.56 tokens max: 32 tokens	min: 14 tokens mean: 19.41 tokens max: 31 tokens	min: 14 tokens mean: 19.23 tokens max: 28 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task400_paws_paraphrase_classification

• Dataset: task400_paws_paraphrase_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 19 tokens mean: 52.36 tokens max: 97 tokens	min: 18 tokens mean: 51.95 tokens max: 98 tokens	min: 19 tokens mean: 53.15 tokens max: 97 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task502_scruples_anecdotes_whoiswrong_verification

• Dataset: task502_scruples_anecdotes_whoiswrong_verification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 12 tokens mean: 230.23 tokens max: 256 tokens	min: 12 tokens mean: 237.36 tokens max: 256 tokens	min: 23 tokens mean: 235.54 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task088_identify_typo_verification

• Dataset: task088_identify_typo_verification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 11 tokens mean: 15.08 tokens max: 48 tokens	min: 10 tokens mean: 15.08 tokens max: 47 tokens	min: 10 tokens mean: 15.44 tokens max: 47 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task221_rocstories_two_choice_classification

• Dataset: task221_rocstories_two_choice_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 47 tokens mean: 72.48 tokens max: 108 tokens	min: 48 tokens mean: 72.62 tokens max: 109 tokens	min: 46 tokens mean: 73.09 tokens max: 108 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task200_mnli_entailment_classification

• Dataset: task200_mnli_entailment_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 24 tokens mean: 72.76 tokens max: 198 tokens	min: 23 tokens mean: 72.79 tokens max: 224 tokens	min: 23 tokens mean: 73.78 tokens max: 226 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task074_squad1.1_question_generation

• Dataset: task074_squad1.1_question_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 30 tokens mean: 150.54 tokens max: 256 tokens	min: 33 tokens mean: 160.14 tokens max: 256 tokens	min: 38 tokens mean: 164.82 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task581_socialiqa_question_generation

• Dataset: task581_socialiqa_question_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 12 tokens mean: 26.46 tokens max: 69 tokens	min: 14 tokens mean: 25.62 tokens max: 48 tokens	min: 15 tokens mean: 25.77 tokens max: 48 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1186_nne_hrngo_classification

• Dataset: task1186_nne_hrngo_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 19 tokens mean: 33.82 tokens max: 79 tokens	min: 19 tokens mean: 33.54 tokens max: 74 tokens	min: 20 tokens mean: 33.61 tokens max: 77 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task898_freebase_qa_answer_generation

• Dataset: task898_freebase_qa_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 8 tokens mean: 19.28 tokens max: 125 tokens	min: 8 tokens mean: 17.6 tokens max: 49 tokens	min: 8 tokens mean: 17.48 tokens max: 79 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1408_dart_similarity_classification

• Dataset: task1408_dart_similarity_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 22 tokens mean: 59.38 tokens max: 147 tokens	min: 22 tokens mean: 61.9 tokens max: 152 tokens	min: 20 tokens mean: 48.71 tokens max: 124 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task168_strategyqa_question_decomposition

• Dataset: task168_strategyqa_question_decomposition
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 42 tokens mean: 80.29 tokens max: 181 tokens	min: 42 tokens mean: 79.81 tokens max: 179 tokens	min: 42 tokens mean: 76.56 tokens max: 166 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1357_xlsum_summary_generation

• Dataset: task1357_xlsum_summary_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 67 tokens mean: 241.43 tokens max: 256 tokens	min: 69 tokens mean: 242.81 tokens max: 256 tokens	min: 67 tokens mean: 246.9 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task390_torque_text_span_selection

• Dataset: task390_torque_text_span_selection
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 47 tokens mean: 110.17 tokens max: 195 tokens	min: 42 tokens mean: 110.05 tokens max: 195 tokens	min: 48 tokens mean: 111.11 tokens max: 193 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task165_mcscript_question_answering_commonsense

• Dataset: task165_mcscript_question_answering_commonsense
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 147 tokens mean: 198.74 tokens max: 256 tokens	min: 145 tokens mean: 197.1 tokens max: 256 tokens	min: 147 tokens mean: 199.01 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1533_daily_dialog_formal_classification

• Dataset: task1533_daily_dialog_formal_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 13 tokens mean: 129.87 tokens max: 256 tokens	min: 15 tokens mean: 136.15 tokens max: 256 tokens	min: 17 tokens mean: 137.06 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task002_quoref_answer_generation

• Dataset: task002_quoref_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 214 tokens mean: 255.59 tokens max: 256 tokens	min: 214 tokens mean: 255.56 tokens max: 256 tokens	min: 224 tokens mean: 255.64 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1297_qasc_question_answering

• Dataset: task1297_qasc_question_answering
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 61 tokens mean: 84.68 tokens max: 134 tokens	min: 59 tokens mean: 85.37 tokens max: 130 tokens	min: 58 tokens mean: 85.03 tokens max: 125 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task305_jeopardy_answer_generation_normal

• Dataset: task305_jeopardy_answer_generation_normal
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 9 tokens mean: 27.73 tokens max: 59 tokens	min: 9 tokens mean: 27.43 tokens max: 45 tokens	min: 11 tokens mean: 27.44 tokens max: 46 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task029_winogrande_full_object

• Dataset: task029_winogrande_full_object
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 7.37 tokens max: 12 tokens	min: 7 tokens mean: 7.34 tokens max: 11 tokens	min: 7 tokens mean: 7.23 tokens max: 10 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1327_qa_zre_answer_generation_from_question

• Dataset: task1327_qa_zre_answer_generation_from_question
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 24 tokens mean: 54.7 tokens max: 256 tokens	min: 23 tokens mean: 52.15 tokens max: 256 tokens	min: 27 tokens mean: 55.41 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task326_jigsaw_classification_obscene

• Dataset: task326_jigsaw_classification_obscene
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 64.2 tokens max: 256 tokens	min: 5 tokens mean: 76.63 tokens max: 256 tokens	min: 5 tokens mean: 73.25 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1542_every_ith_element_from_starting

• Dataset: task1542_every_ith_element_from_starting
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 13 tokens mean: 125.5 tokens max: 245 tokens	min: 13 tokens mean: 123.86 tokens max: 244 tokens	min: 13 tokens mean: 121.61 tokens max: 238 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task570_recipe_nlg_ner_generation

• Dataset: task570_recipe_nlg_ner_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 9 tokens mean: 74.36 tokens max: 250 tokens	min: 5 tokens mean: 73.92 tokens max: 256 tokens	min: 8 tokens mean: 76.49 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1409_dart_text_generation

• Dataset: task1409_dart_text_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 18 tokens mean: 67.69 tokens max: 174 tokens	min: 18 tokens mean: 72.44 tokens max: 170 tokens	min: 17 tokens mean: 67.6 tokens max: 164 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task401_numeric_fused_head_reference

• Dataset: task401_numeric_fused_head_reference
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 16 tokens mean: 108.17 tokens max: 256 tokens	min: 16 tokens mean: 115.09 tokens max: 256 tokens	min: 18 tokens mean: 118.9 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task846_pubmedqa_classification

• Dataset: task846_pubmedqa_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 32 tokens mean: 85.57 tokens max: 246 tokens	min: 33 tokens mean: 85.35 tokens max: 225 tokens	min: 28 tokens mean: 93.84 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1712_poki_classification

• Dataset: task1712_poki_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 53.03 tokens max: 256 tokens	min: 7 tokens mean: 56.22 tokens max: 256 tokens	min: 7 tokens mean: 63.53 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task344_hybridqa_answer_generation

• Dataset: task344_hybridqa_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 9 tokens mean: 22.25 tokens max: 50 tokens	min: 8 tokens mean: 22.03 tokens max: 58 tokens	min: 7 tokens mean: 22.09 tokens max: 55 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task875_emotion_classification

• Dataset: task875_emotion_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 23.26 tokens max: 75 tokens	min: 4 tokens mean: 18.43 tokens max: 63 tokens	min: 5 tokens mean: 20.45 tokens max: 68 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1214_atomic_classification_xwant

• Dataset: task1214_atomic_classification_xwant
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 14 tokens mean: 19.63 tokens max: 32 tokens	min: 14 tokens mean: 19.4 tokens max: 29 tokens	min: 14 tokens mean: 19.51 tokens max: 31 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task106_scruples_ethical_judgment

• Dataset: task106_scruples_ethical_judgment
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 12 tokens mean: 29.96 tokens max: 70 tokens	min: 14 tokens mean: 28.9 tokens max: 86 tokens	min: 14 tokens mean: 28.74 tokens max: 58 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task238_iirc_answer_from_passage_answer_generation

• Dataset: task238_iirc_answer_from_passage_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 138 tokens mean: 242.6 tokens max: 256 tokens	min: 165 tokens mean: 242.73 tokens max: 256 tokens	min: 173 tokens mean: 242.75 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1391_winogrande_easy_answer_generation

• Dataset: task1391_winogrande_easy_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 26 tokens mean: 31.69 tokens max: 54 tokens	min: 26 tokens mean: 31.33 tokens max: 48 tokens	min: 25 tokens mean: 31.24 tokens max: 49 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task195_sentiment140_classification

• Dataset: task195_sentiment140_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 22.54 tokens max: 118 tokens	min: 4 tokens mean: 18.88 tokens max: 79 tokens	min: 5 tokens mean: 21.21 tokens max: 51 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task163_count_words_ending_with_letter

• Dataset: task163_count_words_ending_with_letter
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 28 tokens mean: 32.02 tokens max: 54 tokens	min: 28 tokens mean: 31.67 tokens max: 57 tokens	min: 28 tokens mean: 31.61 tokens max: 43 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task579_socialiqa_classification

• Dataset: task579_socialiqa_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 39 tokens mean: 54.05 tokens max: 132 tokens	min: 36 tokens mean: 53.63 tokens max: 103 tokens	min: 40 tokens mean: 54.14 tokens max: 84 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task569_recipe_nlg_text_generation

• Dataset: task569_recipe_nlg_text_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 25 tokens mean: 192.79 tokens max: 256 tokens	min: 55 tokens mean: 193.37 tokens max: 256 tokens	min: 37 tokens mean: 198.66 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1602_webquestion_question_genreation

• Dataset: task1602_webquestion_question_genreation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 12 tokens mean: 23.49 tokens max: 112 tokens	min: 12 tokens mean: 24.22 tokens max: 112 tokens	min: 12 tokens mean: 22.38 tokens max: 120 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task747_glucose_cause_emotion_detection

• Dataset: task747_glucose_cause_emotion_detection
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 35 tokens mean: 68.11 tokens max: 112 tokens	min: 36 tokens mean: 68.21 tokens max: 108 tokens	min: 36 tokens mean: 68.9 tokens max: 99 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task219_rocstories_title_answer_generation

• Dataset: task219_rocstories_title_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 42 tokens mean: 67.56 tokens max: 97 tokens	min: 45 tokens mean: 66.68 tokens max: 97 tokens	min: 41 tokens mean: 66.69 tokens max: 94 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task178_quartz_question_answering

• Dataset: task178_quartz_question_answering
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 28 tokens mean: 57.7 tokens max: 110 tokens	min: 28 tokens mean: 57.22 tokens max: 111 tokens	min: 28 tokens mean: 56.71 tokens max: 102 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task103_facts2story_long_text_generation

• Dataset: task103_facts2story_long_text_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 52 tokens mean: 80.4 tokens max: 143 tokens	min: 51 tokens mean: 82.34 tokens max: 157 tokens	min: 49 tokens mean: 78.91 tokens max: 145 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task301_record_question_generation

• Dataset: task301_record_question_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 140 tokens mean: 210.26 tokens max: 256 tokens	min: 139 tokens mean: 209.33 tokens max: 256 tokens	min: 143 tokens mean: 208.9 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1369_healthfact_sentence_generation

• Dataset: task1369_healthfact_sentence_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 110 tokens mean: 242.91 tokens max: 256 tokens	min: 101 tokens mean: 242.9 tokens max: 256 tokens	min: 113 tokens mean: 251.71 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task515_senteval_odd_word_out

• Dataset: task515_senteval_odd_word_out
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 19.77 tokens max: 36 tokens	min: 7 tokens mean: 19.14 tokens max: 38 tokens	min: 7 tokens mean: 19.01 tokens max: 35 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task496_semeval_answer_generation

• Dataset: task496_semeval_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 28.06 tokens max: 46 tokens	min: 18 tokens mean: 27.75 tokens max: 45 tokens	min: 19 tokens mean: 27.67 tokens max: 45 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1658_billsum_summarization

• Dataset: task1658_billsum_summarization
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 256 tokens mean: 256.0 tokens max: 256 tokens	min: 256 tokens mean: 256.0 tokens max: 256 tokens	min: 256 tokens mean: 256.0 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1204_atomic_classification_hinderedby

• Dataset: task1204_atomic_classification_hinderedby
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 14 tokens mean: 22.03 tokens max: 35 tokens	min: 14 tokens mean: 22.02 tokens max: 34 tokens	min: 14 tokens mean: 21.57 tokens max: 38 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1392_superglue_multirc_answer_verification

• Dataset: task1392_superglue_multirc_answer_verification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 128 tokens mean: 241.64 tokens max: 256 tokens	min: 127 tokens mean: 241.89 tokens max: 256 tokens	min: 136 tokens mean: 241.92 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task306_jeopardy_answer_generation_double

• Dataset: task306_jeopardy_answer_generation_double
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 10 tokens mean: 27.76 tokens max: 47 tokens	min: 10 tokens mean: 27.05 tokens max: 46 tokens	min: 11 tokens mean: 27.53 tokens max: 47 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1286_openbookqa_question_answering

• Dataset: task1286_openbookqa_question_answering
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 22 tokens mean: 39.6 tokens max: 85 tokens	min: 23 tokens mean: 38.95 tokens max: 96 tokens	min: 22 tokens mean: 38.49 tokens max: 89 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task159_check_frequency_of_words_in_sentence_pair

• Dataset: task159_check_frequency_of_words_in_sentence_pair
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 44 tokens mean: 50.34 tokens max: 67 tokens	min: 44 tokens mean: 50.31 tokens max: 67 tokens	min: 44 tokens mean: 50.55 tokens max: 66 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task151_tomqa_find_location_easy_clean

• Dataset: task151_tomqa_find_location_easy_clean
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 37 tokens mean: 50.73 tokens max: 79 tokens	min: 37 tokens mean: 50.33 tokens max: 74 tokens	min: 37 tokens mean: 50.67 tokens max: 74 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task323_jigsaw_classification_sexually_explicit

• Dataset: task323_jigsaw_classification_sexually_explicit
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 66.36 tokens max: 248 tokens	min: 5 tokens mean: 77.8 tokens max: 248 tokens	min: 6 tokens mean: 75.28 tokens max: 251 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task037_qasc_generate_related_fact

• Dataset: task037_qasc_generate_related_fact
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 13 tokens mean: 22.01 tokens max: 50 tokens	min: 13 tokens mean: 21.96 tokens max: 42 tokens	min: 13 tokens mean: 21.81 tokens max: 40 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task027_drop_answer_type_generation

• Dataset: task027_drop_answer_type_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 87 tokens mean: 228.71 tokens max: 256 tokens	min: 74 tokens mean: 230.45 tokens max: 256 tokens	min: 71 tokens mean: 232.07 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1596_event2mind_text_generation_2

• Dataset: task1596_event2mind_text_generation_2
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 9.96 tokens max: 18 tokens	min: 6 tokens mean: 10.01 tokens max: 19 tokens	min: 6 tokens mean: 10.01 tokens max: 18 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task141_odd-man-out_classification_category

• Dataset: task141_odd-man-out_classification_category
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 16 tokens mean: 18.44 tokens max: 28 tokens	min: 16 tokens mean: 18.38 tokens max: 26 tokens	min: 16 tokens mean: 18.45 tokens max: 25 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task194_duorc_answer_generation

• Dataset: task194_duorc_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 149 tokens mean: 251.79 tokens max: 256 tokens	min: 147 tokens mean: 252.02 tokens max: 256 tokens	min: 148 tokens mean: 251.79 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task679_hope_edi_english_text_classification

• Dataset: task679_hope_edi_english_text_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 27.4 tokens max: 199 tokens	min: 4 tokens mean: 26.88 tokens max: 205 tokens	min: 5 tokens mean: 29.43 tokens max: 194 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task246_dream_question_generation

• Dataset: task246_dream_question_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 17 tokens mean: 81.3 tokens max: 256 tokens	min: 14 tokens mean: 82.42 tokens max: 256 tokens	min: 15 tokens mean: 88.05 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1195_disflqa_disfluent_to_fluent_conversion

• Dataset: task1195_disflqa_disfluent_to_fluent_conversion
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 9 tokens mean: 19.84 tokens max: 41 tokens	min: 9 tokens mean: 19.79 tokens max: 40 tokens	min: 6 tokens mean: 20.15 tokens max: 44 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task065_timetravel_consistent_sentence_classification

• Dataset: task065_timetravel_consistent_sentence_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 55 tokens mean: 79.5 tokens max: 117 tokens	min: 51 tokens mean: 79.21 tokens max: 110 tokens	min: 53 tokens mean: 79.75 tokens max: 110 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task351_winomt_classification_gender_identifiability_anti

• Dataset: task351_winomt_classification_gender_identifiability_anti
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 16 tokens mean: 21.8 tokens max: 30 tokens	min: 16 tokens mean: 21.65 tokens max: 31 tokens	min: 16 tokens mean: 21.82 tokens max: 30 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task580_socialiqa_answer_generation

• Dataset: task580_socialiqa_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 35 tokens mean: 52.36 tokens max: 107 tokens	min: 35 tokens mean: 51.06 tokens max: 86 tokens	min: 35 tokens mean: 51.05 tokens max: 87 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task583_udeps_eng_coarse_pos_tagging

• Dataset: task583_udeps_eng_coarse_pos_tagging
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 12 tokens mean: 40.98 tokens max: 185 tokens	min: 12 tokens mean: 39.98 tokens max: 185 tokens	min: 12 tokens mean: 40.84 tokens max: 185 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task202_mnli_contradiction_classification

• Dataset: task202_mnli_contradiction_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 24 tokens mean: 74.09 tokens max: 190 tokens	min: 28 tokens mean: 76.62 tokens max: 256 tokens	min: 23 tokens mean: 74.97 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task222_rocstories_two_chioce_slotting_classification

• Dataset: task222_rocstories_two_chioce_slotting_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 48 tokens mean: 73.05 tokens max: 105 tokens	min: 48 tokens mean: 73.11 tokens max: 100 tokens	min: 49 tokens mean: 72.03 tokens max: 102 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task498_scruples_anecdotes_whoiswrong_classification

• Dataset: task498_scruples_anecdotes_whoiswrong_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 24 tokens mean: 226.01 tokens max: 256 tokens	min: 47 tokens mean: 232.19 tokens max: 256 tokens	min: 47 tokens mean: 231.44 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task067_abductivenli_answer_generation

• Dataset: task067_abductivenli_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 14 tokens mean: 26.68 tokens max: 40 tokens	min: 14 tokens mean: 26.08 tokens max: 42 tokens	min: 15 tokens mean: 26.31 tokens max: 38 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task616_cola_classification

• Dataset: task616_cola_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 12.55 tokens max: 33 tokens	min: 5 tokens mean: 12.35 tokens max: 33 tokens	min: 6 tokens mean: 12.15 tokens max: 29 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task286_olid_offense_judgment

• Dataset: task286_olid_offense_judgment
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 33.01 tokens max: 145 tokens	min: 5 tokens mean: 30.8 tokens max: 171 tokens	min: 5 tokens mean: 30.5 tokens max: 169 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task188_snli_neutral_to_entailment_text_modification

• Dataset: task188_snli_neutral_to_entailment_text_modification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 18 tokens mean: 31.73 tokens max: 79 tokens	min: 18 tokens mean: 31.19 tokens max: 84 tokens	min: 18 tokens mean: 33.07 tokens max: 84 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task223_quartz_explanation_generation

• Dataset: task223_quartz_explanation_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 12 tokens mean: 31.42 tokens max: 68 tokens	min: 13 tokens mean: 31.78 tokens max: 68 tokens	min: 13 tokens mean: 29.14 tokens max: 96 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task820_protoqa_answer_generation

• Dataset: task820_protoqa_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 14.72 tokens max: 29 tokens	min: 7 tokens mean: 14.5 tokens max: 27 tokens	min: 6 tokens mean: 14.14 tokens max: 29 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task196_sentiment140_answer_generation

• Dataset: task196_sentiment140_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 17 tokens mean: 36.05 tokens max: 72 tokens	min: 17 tokens mean: 33.0 tokens max: 61 tokens	min: 17 tokens mean: 36.04 tokens max: 72 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1678_mathqa_answer_selection

• Dataset: task1678_mathqa_answer_selection
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 33 tokens mean: 70.4 tokens max: 177 tokens	min: 30 tokens mean: 69.17 tokens max: 146 tokens	min: 33 tokens mean: 69.59 tokens max: 160 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task349_squad2.0_answerable_unanswerable_question_classification

• Dataset: task349_squad2.0_answerable_unanswerable_question_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 53 tokens mean: 175.82 tokens max: 256 tokens	min: 57 tokens mean: 175.84 tokens max: 256 tokens	min: 53 tokens mean: 175.75 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task154_tomqa_find_location_hard_noise

• Dataset: task154_tomqa_find_location_hard_noise
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 129 tokens mean: 175.98 tokens max: 253 tokens	min: 126 tokens mean: 175.79 tokens max: 249 tokens	min: 128 tokens mean: 178.03 tokens max: 254 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task333_hateeval_classification_hate_en

• Dataset: task333_hateeval_classification_hate_en
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 8 tokens mean: 38.36 tokens max: 117 tokens	min: 7 tokens mean: 37.23 tokens max: 109 tokens	min: 7 tokens mean: 36.91 tokens max: 113 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task235_iirc_question_from_subtext_answer_generation

• Dataset: task235_iirc_question_from_subtext_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 14 tokens mean: 53.19 tokens max: 256 tokens	min: 12 tokens mean: 50.68 tokens max: 256 tokens	min: 12 tokens mean: 56.08 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1554_scitail_classification

• Dataset: task1554_scitail_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 16.75 tokens max: 38 tokens	min: 7 tokens mean: 25.66 tokens max: 68 tokens	min: 7 tokens mean: 24.24 tokens max: 59 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task210_logic2text_structured_text_generation

• Dataset: task210_logic2text_structured_text_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 13 tokens mean: 31.42 tokens max: 101 tokens	min: 13 tokens mean: 30.58 tokens max: 94 tokens	min: 12 tokens mean: 32.71 tokens max: 89 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task035_winogrande_question_modification_person

• Dataset: task035_winogrande_question_modification_person
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 31 tokens mean: 36.21 tokens max: 50 tokens	min: 31 tokens mean: 35.78 tokens max: 55 tokens	min: 31 tokens mean: 35.43 tokens max: 48 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task230_iirc_passage_classification

• Dataset: task230_iirc_passage_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 256 tokens mean: 256.0 tokens max: 256 tokens	min: 256 tokens mean: 256.0 tokens max: 256 tokens	min: 256 tokens mean: 256.0 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1356_xlsum_title_generation

• Dataset: task1356_xlsum_title_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 59 tokens mean: 239.7 tokens max: 256 tokens	min: 58 tokens mean: 240.91 tokens max: 256 tokens	min: 64 tokens mean: 248.19 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1726_mathqa_correct_answer_generation

• Dataset: task1726_mathqa_correct_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 10 tokens mean: 43.96 tokens max: 156 tokens	min: 12 tokens mean: 42.39 tokens max: 129 tokens	min: 11 tokens mean: 42.76 tokens max: 133 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task302_record_classification

• Dataset: task302_record_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 194 tokens mean: 253.3 tokens max: 256 tokens	min: 198 tokens mean: 252.87 tokens max: 256 tokens	min: 195 tokens mean: 252.88 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task380_boolq_yes_no_question

• Dataset: task380_boolq_yes_no_question
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 26 tokens mean: 133.55 tokens max: 256 tokens	min: 26 tokens mean: 138.51 tokens max: 256 tokens	min: 27 tokens mean: 137.7 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task212_logic2text_classification

• Dataset: task212_logic2text_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 14 tokens mean: 33.28 tokens max: 146 tokens	min: 14 tokens mean: 32.18 tokens max: 146 tokens	min: 14 tokens mean: 33.05 tokens max: 127 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task748_glucose_reverse_cause_event_detection

• Dataset: task748_glucose_reverse_cause_event_detection
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 35 tokens mean: 67.79 tokens max: 105 tokens	min: 38 tokens mean: 67.02 tokens max: 106 tokens	min: 39 tokens mean: 68.89 tokens max: 105 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task834_mathdataset_classification

• Dataset: task834_mathdataset_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 27.66 tokens max: 83 tokens	min: 6 tokens mean: 27.97 tokens max: 83 tokens	min: 5 tokens mean: 26.81 tokens max: 93 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task350_winomt_classification_gender_identifiability_pro

• Dataset: task350_winomt_classification_gender_identifiability_pro
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 16 tokens mean: 21.79 tokens max: 30 tokens	min: 16 tokens mean: 21.6 tokens max: 30 tokens	min: 16 tokens mean: 21.79 tokens max: 30 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task191_hotpotqa_question_generation

• Dataset: task191_hotpotqa_question_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 198 tokens mean: 255.9 tokens max: 256 tokens	min: 238 tokens mean: 255.94 tokens max: 256 tokens	min: 255 tokens mean: 256.0 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task236_iirc_question_from_passage_answer_generation

• Dataset: task236_iirc_question_from_passage_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 135 tokens mean: 237.8 tokens max: 256 tokens	min: 155 tokens mean: 237.37 tokens max: 256 tokens	min: 154 tokens mean: 239.52 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task217_rocstories_ordering_answer_generation

• Dataset: task217_rocstories_ordering_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 45 tokens mean: 72.31 tokens max: 107 tokens	min: 48 tokens mean: 72.45 tokens max: 107 tokens	min: 48 tokens mean: 70.93 tokens max: 105 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task568_circa_question_generation

• Dataset: task568_circa_question_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 9.58 tokens max: 25 tokens	min: 4 tokens mean: 9.46 tokens max: 20 tokens	min: 4 tokens mean: 8.96 tokens max: 20 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task614_glucose_cause_event_detection

• Dataset: task614_glucose_cause_event_detection
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 39 tokens mean: 68.04 tokens max: 102 tokens	min: 39 tokens mean: 67.48 tokens max: 106 tokens	min: 38 tokens mean: 68.47 tokens max: 103 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task361_spolin_yesand_prompt_response_classification

• Dataset: task361_spolin_yesand_prompt_response_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 18 tokens mean: 46.91 tokens max: 137 tokens	min: 17 tokens mean: 45.88 tokens max: 119 tokens	min: 17 tokens mean: 47.26 tokens max: 128 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task421_persent_sentence_sentiment_classification

• Dataset: task421_persent_sentence_sentiment_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 22 tokens mean: 67.54 tokens max: 256 tokens	min: 22 tokens mean: 70.48 tokens max: 256 tokens	min: 19 tokens mean: 72.1 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task203_mnli_sentence_generation

• Dataset: task203_mnli_sentence_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 14 tokens mean: 39.11 tokens max: 175 tokens	min: 14 tokens mean: 35.71 tokens max: 175 tokens	min: 13 tokens mean: 33.79 tokens max: 170 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task420_persent_document_sentiment_classification

• Dataset: task420_persent_document_sentiment_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 22 tokens mean: 222.93 tokens max: 256 tokens	min: 22 tokens mean: 233.66 tokens max: 256 tokens	min: 22 tokens mean: 228.59 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task153_tomqa_find_location_hard_clean

• Dataset: task153_tomqa_find_location_hard_clean
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 39 tokens mean: 161.82 tokens max: 256 tokens	min: 39 tokens mean: 160.65 tokens max: 256 tokens	min: 39 tokens mean: 163.76 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task346_hybridqa_classification

• Dataset: task346_hybridqa_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 18 tokens mean: 32.92 tokens max: 68 tokens	min: 18 tokens mean: 32.04 tokens max: 63 tokens	min: 19 tokens mean: 31.83 tokens max: 75 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1211_atomic_classification_hassubevent

• Dataset: task1211_atomic_classification_hassubevent
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 11 tokens mean: 16.25 tokens max: 31 tokens	min: 11 tokens mean: 16.04 tokens max: 29 tokens	min: 11 tokens mean: 16.85 tokens max: 29 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task360_spolin_yesand_response_generation

• Dataset: task360_spolin_yesand_response_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 22.42 tokens max: 89 tokens	min: 6 tokens mean: 21.07 tokens max: 92 tokens	min: 7 tokens mean: 20.79 tokens max: 67 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task510_reddit_tifu_title_summarization

• Dataset: task510_reddit_tifu_title_summarization
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 9 tokens mean: 217.56 tokens max: 256 tokens	min: 20 tokens mean: 218.54 tokens max: 256 tokens	min: 10 tokens mean: 222.3 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task511_reddit_tifu_long_text_summarization

• Dataset: task511_reddit_tifu_long_text_summarization
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 29 tokens mean: 239.85 tokens max: 256 tokens	min: 76 tokens mean: 239.22 tokens max: 256 tokens	min: 43 tokens mean: 244.78 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task345_hybridqa_answer_generation

• Dataset: task345_hybridqa_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 9 tokens mean: 22.18 tokens max: 50 tokens	min: 10 tokens mean: 21.6 tokens max: 70 tokens	min: 8 tokens mean: 20.93 tokens max: 47 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task270_csrg_counterfactual_context_generation

• Dataset: task270_csrg_counterfactual_context_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 63 tokens mean: 99.9 tokens max: 158 tokens	min: 63 tokens mean: 98.48 tokens max: 142 tokens	min: 62 tokens mean: 100.17 tokens max: 141 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task307_jeopardy_answer_generation_final

• Dataset: task307_jeopardy_answer_generation_final
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 15 tokens mean: 29.61 tokens max: 46 tokens	min: 15 tokens mean: 29.3 tokens max: 53 tokens	min: 15 tokens mean: 29.22 tokens max: 43 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task001_quoref_question_generation

• Dataset: task001_quoref_question_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 201 tokens mean: 255.05 tokens max: 256 tokens	min: 99 tokens mean: 254.34 tokens max: 256 tokens	min: 173 tokens mean: 255.12 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task089_swap_words_verification

• Dataset: task089_swap_words_verification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 9 tokens mean: 12.96 tokens max: 28 tokens	min: 9 tokens mean: 12.65 tokens max: 24 tokens	min: 9 tokens mean: 12.28 tokens max: 22 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1196_atomic_classification_oeffect

• Dataset: task1196_atomic_classification_oeffect
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 14 tokens mean: 18.71 tokens max: 38 tokens	min: 14 tokens mean: 18.53 tokens max: 30 tokens	min: 14 tokens mean: 18.45 tokens max: 29 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task080_piqa_answer_generation

• Dataset: task080_piqa_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 3 tokens mean: 10.85 tokens max: 33 tokens	min: 3 tokens mean: 10.73 tokens max: 24 tokens	min: 3 tokens mean: 10.07 tokens max: 26 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1598_nyc_long_text_generation

• Dataset: task1598_nyc_long_text_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 17 tokens mean: 35.51 tokens max: 56 tokens	min: 17 tokens mean: 35.66 tokens max: 56 tokens	min: 20 tokens mean: 36.62 tokens max: 55 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task240_tweetqa_question_generation

• Dataset: task240_tweetqa_question_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 27 tokens mean: 51.2 tokens max: 94 tokens	min: 25 tokens mean: 50.85 tokens max: 92 tokens	min: 20 tokens mean: 51.59 tokens max: 95 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task615_moviesqa_answer_generation

• Dataset: task615_moviesqa_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 11.48 tokens max: 23 tokens	min: 7 tokens mean: 11.47 tokens max: 19 tokens	min: 5 tokens mean: 11.42 tokens max: 22 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1347_glue_sts-b_similarity_classification

• Dataset: task1347_glue_sts-b_similarity_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 17 tokens mean: 31.02 tokens max: 88 tokens	min: 16 tokens mean: 30.94 tokens max: 92 tokens	min: 16 tokens mean: 30.77 tokens max: 92 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task114_is_the_given_word_longest

• Dataset: task114_is_the_given_word_longest
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 25 tokens mean: 28.86 tokens max: 68 tokens	min: 25 tokens mean: 28.45 tokens max: 48 tokens	min: 25 tokens mean: 28.71 tokens max: 47 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task292_storycommonsense_character_text_generation

• Dataset: task292_storycommonsense_character_text_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 43 tokens mean: 68.03 tokens max: 98 tokens	min: 46 tokens mean: 67.15 tokens max: 104 tokens	min: 43 tokens mean: 68.95 tokens max: 96 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task115_help_advice_classification

• Dataset: task115_help_advice_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 2 tokens mean: 19.78 tokens max: 91 tokens	min: 3 tokens mean: 18.26 tokens max: 92 tokens	min: 4 tokens mean: 19.23 tokens max: 137 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task431_senteval_object_count

• Dataset: task431_senteval_object_count
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 16.81 tokens max: 37 tokens	min: 7 tokens mean: 15.13 tokens max: 36 tokens	min: 7 tokens mean: 15.85 tokens max: 35 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1360_numer_sense_multiple_choice_qa_generation

• Dataset: task1360_numer_sense_multiple_choice_qa_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 32 tokens mean: 40.61 tokens max: 54 tokens	min: 32 tokens mean: 40.28 tokens max: 53 tokens	min: 32 tokens mean: 40.22 tokens max: 60 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task177_para-nmt_paraphrasing

• Dataset: task177_para-nmt_paraphrasing
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 8 tokens mean: 19.96 tokens max: 82 tokens	min: 9 tokens mean: 19.01 tokens max: 58 tokens	min: 9 tokens mean: 18.26 tokens max: 36 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task132_dais_text_modification

• Dataset: task132_dais_text_modification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 9.31 tokens max: 15 tokens	min: 6 tokens mean: 9.06 tokens max: 15 tokens	min: 6 tokens mean: 10.18 tokens max: 15 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task269_csrg_counterfactual_story_generation

• Dataset: task269_csrg_counterfactual_story_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 49 tokens mean: 79.85 tokens max: 111 tokens	min: 53 tokens mean: 79.61 tokens max: 116 tokens	min: 48 tokens mean: 79.41 tokens max: 114 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task233_iirc_link_exists_classification

• Dataset: task233_iirc_link_exists_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 145 tokens mean: 235.94 tokens max: 256 tokens	min: 142 tokens mean: 233.51 tokens max: 256 tokens	min: 151 tokens mean: 235.02 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task161_count_words_containing_letter

• Dataset: task161_count_words_containing_letter
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 27 tokens mean: 31.0 tokens max: 53 tokens	min: 27 tokens mean: 30.75 tokens max: 61 tokens	min: 27 tokens mean: 30.49 tokens max: 42 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1205_atomic_classification_isafter

• Dataset: task1205_atomic_classification_isafter
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 14 tokens mean: 20.86 tokens max: 37 tokens	min: 14 tokens mean: 20.57 tokens max: 31 tokens	min: 14 tokens mean: 21.47 tokens max: 37 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task571_recipe_nlg_ner_generation

• Dataset: task571_recipe_nlg_ner_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 118.44 tokens max: 256 tokens	min: 7 tokens mean: 118.52 tokens max: 256 tokens	min: 6 tokens mean: 110.48 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1292_yelp_review_full_text_categorization

• Dataset: task1292_yelp_review_full_text_categorization
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 136.54 tokens max: 256 tokens	min: 7 tokens mean: 145.81 tokens max: 256 tokens	min: 3 tokens mean: 145.95 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task428_senteval_inversion

• Dataset: task428_senteval_inversion
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 16.55 tokens max: 32 tokens	min: 7 tokens mean: 14.51 tokens max: 31 tokens	min: 7 tokens mean: 15.29 tokens max: 34 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task311_race_question_generation

• Dataset: task311_race_question_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 115 tokens mean: 254.7 tokens max: 256 tokens	min: 137 tokens mean: 254.34 tokens max: 256 tokens	min: 171 tokens mean: 255.51 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task429_senteval_tense

• Dataset: task429_senteval_tense
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 15.83 tokens max: 37 tokens	min: 6 tokens mean: 14.0 tokens max: 33 tokens	min: 7 tokens mean: 15.24 tokens max: 36 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task403_creak_commonsense_inference

• Dataset: task403_creak_commonsense_inference
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 13 tokens mean: 30.31 tokens max: 104 tokens	min: 13 tokens mean: 29.42 tokens max: 108 tokens	min: 13 tokens mean: 29.44 tokens max: 122 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task929_products_reviews_classification

• Dataset: task929_products_reviews_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 69.3 tokens max: 126 tokens	min: 6 tokens mean: 70.55 tokens max: 123 tokens	min: 6 tokens mean: 70.46 tokens max: 123 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task582_naturalquestion_answer_generation

• Dataset: task582_naturalquestion_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 10 tokens mean: 11.7 tokens max: 25 tokens	min: 10 tokens mean: 11.66 tokens max: 24 tokens	min: 10 tokens mean: 11.72 tokens max: 25 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task237_iirc_answer_from_subtext_answer_generation

• Dataset: task237_iirc_answer_from_subtext_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 22 tokens mean: 66.32 tokens max: 256 tokens	min: 25 tokens mean: 64.83 tokens max: 256 tokens	min: 23 tokens mean: 61.21 tokens max: 161 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task050_multirc_answerability

• Dataset: task050_multirc_answerability
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 15 tokens mean: 32.58 tokens max: 112 tokens	min: 14 tokens mean: 31.57 tokens max: 93 tokens	min: 15 tokens mean: 32.31 tokens max: 159 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task184_break_generate_question

• Dataset: task184_break_generate_question
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 13 tokens mean: 39.54 tokens max: 147 tokens	min: 13 tokens mean: 38.84 tokens max: 149 tokens	min: 13 tokens mean: 39.63 tokens max: 148 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task669_ambigqa_answer_generation

• Dataset: task669_ambigqa_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 10 tokens mean: 12.87 tokens max: 23 tokens	min: 10 tokens mean: 12.87 tokens max: 27 tokens	min: 11 tokens mean: 12.72 tokens max: 22 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task169_strategyqa_sentence_generation

• Dataset: task169_strategyqa_sentence_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 19 tokens mean: 35.19 tokens max: 65 tokens	min: 22 tokens mean: 34.19 tokens max: 60 tokens	min: 19 tokens mean: 33.35 tokens max: 65 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task500_scruples_anecdotes_title_generation

• Dataset: task500_scruples_anecdotes_title_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 14 tokens mean: 224.85 tokens max: 256 tokens	min: 31 tokens mean: 231.86 tokens max: 256 tokens	min: 27 tokens mean: 234.97 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task241_tweetqa_classification

• Dataset: task241_tweetqa_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 31 tokens mean: 61.56 tokens max: 92 tokens	min: 36 tokens mean: 61.79 tokens max: 106 tokens	min: 31 tokens mean: 61.49 tokens max: 92 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1345_glue_qqp_question_paraprashing

• Dataset: task1345_glue_qqp_question_paraprashing
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 16.65 tokens max: 60 tokens	min: 6 tokens mean: 15.77 tokens max: 69 tokens	min: 6 tokens mean: 16.63 tokens max: 51 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task218_rocstories_swap_order_answer_generation

• Dataset: task218_rocstories_swap_order_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 48 tokens mean: 72.71 tokens max: 118 tokens	min: 48 tokens mean: 72.68 tokens max: 102 tokens	min: 47 tokens mean: 72.11 tokens max: 106 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task613_politifact_text_generation

• Dataset: task613_politifact_text_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 24.79 tokens max: 75 tokens	min: 7 tokens mean: 23.59 tokens max: 56 tokens	min: 7 tokens mean: 22.95 tokens max: 61 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1167_penn_treebank_coarse_pos_tagging

• Dataset: task1167_penn_treebank_coarse_pos_tagging
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 16 tokens mean: 53.67 tokens max: 200 tokens	min: 16 tokens mean: 53.53 tokens max: 220 tokens	min: 16 tokens mean: 54.93 tokens max: 202 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1422_mathqa_physics

• Dataset: task1422_mathqa_physics
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 34 tokens mean: 72.64 tokens max: 164 tokens	min: 38 tokens mean: 71.72 tokens max: 157 tokens	min: 39 tokens mean: 72.49 tokens max: 155 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task247_dream_answer_generation

• Dataset: task247_dream_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 38 tokens mean: 159.06 tokens max: 256 tokens	min: 39 tokens mean: 157.24 tokens max: 256 tokens	min: 41 tokens mean: 166.87 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task199_mnli_classification

• Dataset: task199_mnli_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 13 tokens mean: 43.38 tokens max: 127 tokens	min: 11 tokens mean: 44.68 tokens max: 149 tokens	min: 11 tokens mean: 44.11 tokens max: 113 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task164_mcscript_question_answering_text

• Dataset: task164_mcscript_question_answering_text
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 150 tokens mean: 200.82 tokens max: 256 tokens	min: 150 tokens mean: 200.64 tokens max: 256 tokens	min: 142 tokens mean: 201.14 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1541_agnews_classification

• Dataset: task1541_agnews_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 21 tokens mean: 53.24 tokens max: 256 tokens	min: 18 tokens mean: 53.01 tokens max: 256 tokens	min: 18 tokens mean: 53.68 tokens max: 161 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task516_senteval_conjoints_inversion

• Dataset: task516_senteval_conjoints_inversion
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 8 tokens mean: 20.24 tokens max: 34 tokens	min: 8 tokens mean: 19.03 tokens max: 34 tokens	min: 8 tokens mean: 18.94 tokens max: 34 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task294_storycommonsense_motiv_text_generation

• Dataset: task294_storycommonsense_motiv_text_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 14 tokens mean: 40.05 tokens max: 86 tokens	min: 14 tokens mean: 40.57 tokens max: 86 tokens	min: 14 tokens mean: 39.93 tokens max: 86 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task501_scruples_anecdotes_post_type_verification

• Dataset: task501_scruples_anecdotes_post_type_verification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 18 tokens mean: 230.88 tokens max: 256 tokens	min: 12 tokens mean: 235.72 tokens max: 256 tokens	min: 18 tokens mean: 234.73 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task213_rocstories_correct_ending_classification

• Dataset: task213_rocstories_correct_ending_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 62 tokens mean: 86.09 tokens max: 125 tokens	min: 60 tokens mean: 85.53 tokens max: 131 tokens	min: 59 tokens mean: 86.16 tokens max: 131 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task821_protoqa_question_generation

• Dataset: task821_protoqa_question_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 14.65 tokens max: 61 tokens	min: 5 tokens mean: 15.0 tokens max: 35 tokens	min: 5 tokens mean: 13.91 tokens max: 93 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task493_review_polarity_classification

• Dataset: task493_review_polarity_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 18 tokens mean: 100.78 tokens max: 256 tokens	min: 19 tokens mean: 105.95 tokens max: 256 tokens	min: 14 tokens mean: 113.51 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task308_jeopardy_answer_generation_all

• Dataset: task308_jeopardy_answer_generation_all
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 12 tokens mean: 27.87 tokens max: 50 tokens	min: 10 tokens mean: 27.01 tokens max: 44 tokens	min: 9 tokens mean: 27.45 tokens max: 48 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1595_event2mind_text_generation_1

• Dataset: task1595_event2mind_text_generation_1
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 9.9 tokens max: 18 tokens	min: 6 tokens mean: 9.98 tokens max: 20 tokens	min: 6 tokens mean: 10.04 tokens max: 20 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task040_qasc_question_generation

• Dataset: task040_qasc_question_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 8 tokens mean: 15.03 tokens max: 29 tokens	min: 7 tokens mean: 15.1 tokens max: 30 tokens	min: 8 tokens mean: 13.89 tokens max: 32 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task231_iirc_link_classification

• Dataset: task231_iirc_link_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 179 tokens mean: 246.04 tokens max: 256 tokens	min: 170 tokens mean: 246.17 tokens max: 256 tokens	min: 161 tokens mean: 246.82 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1727_wiqa_what_is_the_effect

• Dataset: task1727_wiqa_what_is_the_effect
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 44 tokens mean: 95.23 tokens max: 183 tokens	min: 44 tokens mean: 95.35 tokens max: 185 tokens	min: 43 tokens mean: 95.5 tokens max: 183 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task578_curiosity_dialogs_answer_generation

• Dataset: task578_curiosity_dialogs_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 10 tokens mean: 230.49 tokens max: 256 tokens	min: 118 tokens mean: 235.45 tokens max: 256 tokens	min: 12 tokens mean: 229.42 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task310_race_classification

• Dataset: task310_race_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 101 tokens mean: 254.92 tokens max: 256 tokens	min: 218 tokens mean: 255.8 tokens max: 256 tokens	min: 101 tokens mean: 254.92 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task309_race_answer_generation

• Dataset: task309_race_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 75 tokens mean: 255.03 tokens max: 256 tokens	min: 204 tokens mean: 255.5 tokens max: 256 tokens	min: 75 tokens mean: 255.23 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task379_agnews_topic_classification

• Dataset: task379_agnews_topic_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 20 tokens mean: 54.64 tokens max: 193 tokens	min: 20 tokens mean: 54.59 tokens max: 175 tokens	min: 21 tokens mean: 54.94 tokens max: 187 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task030_winogrande_full_person

• Dataset: task030_winogrande_full_person
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 7.61 tokens max: 12 tokens	min: 7 tokens mean: 7.49 tokens max: 12 tokens	min: 7 tokens mean: 7.38 tokens max: 11 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1540_parsed_pdfs_summarization

• Dataset: task1540_parsed_pdfs_summarization
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 3 tokens mean: 187.93 tokens max: 256 tokens	min: 46 tokens mean: 189.39 tokens max: 256 tokens	min: 3 tokens mean: 192.0 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task039_qasc_find_overlapping_words

• Dataset: task039_qasc_find_overlapping_words
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 16 tokens mean: 30.46 tokens max: 55 tokens	min: 16 tokens mean: 29.97 tokens max: 57 tokens	min: 16 tokens mean: 30.63 tokens max: 60 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1206_atomic_classification_isbefore

• Dataset: task1206_atomic_classification_isbefore
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 14 tokens mean: 21.25 tokens max: 40 tokens	min: 14 tokens mean: 20.8 tokens max: 31 tokens	min: 14 tokens mean: 21.39 tokens max: 31 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task157_count_vowels_and_consonants

• Dataset: task157_count_vowels_and_consonants
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 24 tokens mean: 27.92 tokens max: 41 tokens	min: 24 tokens mean: 27.86 tokens max: 41 tokens	min: 24 tokens mean: 28.32 tokens max: 39 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task339_record_answer_generation

• Dataset: task339_record_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 171 tokens mean: 234.32 tokens max: 256 tokens	min: 171 tokens mean: 233.93 tokens max: 256 tokens	min: 171 tokens mean: 232.17 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task453_swag_answer_generation

• Dataset: task453_swag_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 9 tokens mean: 18.36 tokens max: 60 tokens	min: 9 tokens mean: 18.27 tokens max: 63 tokens	min: 9 tokens mean: 17.45 tokens max: 55 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task848_pubmedqa_classification

• Dataset: task848_pubmedqa_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 21 tokens mean: 248.87 tokens max: 256 tokens	min: 21 tokens mean: 249.82 tokens max: 256 tokens	min: 84 tokens mean: 251.83 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task673_google_wellformed_query_classification

• Dataset: task673_google_wellformed_query_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 11.62 tokens max: 27 tokens	min: 6 tokens mean: 11.27 tokens max: 24 tokens	min: 6 tokens mean: 11.36 tokens max: 22 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task676_ollie_relationship_answer_generation

• Dataset: task676_ollie_relationship_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 29 tokens mean: 51.03 tokens max: 113 tokens	min: 29 tokens mean: 49.06 tokens max: 134 tokens	min: 30 tokens mean: 51.59 tokens max: 113 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task268_casehold_legal_answer_generation

• Dataset: task268_casehold_legal_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 235 tokens mean: 255.93 tokens max: 256 tokens	min: 156 tokens mean: 255.47 tokens max: 256 tokens	min: 226 tokens mean: 255.95 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task844_financial_phrasebank_classification

• Dataset: task844_financial_phrasebank_classification
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 14 tokens mean: 40.25 tokens max: 86 tokens	min: 13 tokens mean: 38.48 tokens max: 78 tokens	min: 15 tokens mean: 39.15 tokens max: 86 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task330_gap_answer_generation

• Dataset: task330_gap_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 26 tokens mean: 106.97 tokens max: 256 tokens	min: 44 tokens mean: 108.61 tokens max: 256 tokens	min: 45 tokens mean: 111.45 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task595_mocha_answer_generation

• Dataset: task595_mocha_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 44 tokens mean: 94.19 tokens max: 178 tokens	min: 21 tokens mean: 96.04 tokens max: 256 tokens	min: 19 tokens mean: 118.44 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task1285_kpa_keypoint_matching

• Dataset: task1285_kpa_keypoint_matching
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 30 tokens mean: 52.11 tokens max: 92 tokens	min: 29 tokens mean: 50.04 tokens max: 84 tokens	min: 31 tokens mean: 52.95 tokens max: 88 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task234_iirc_passage_line_answer_generation

• Dataset: task234_iirc_passage_line_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 143 tokens mean: 234.7 tokens max: 256 tokens	min: 155 tokens mean: 235.34 tokens max: 256 tokens	min: 146 tokens mean: 236.71 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task494_review_polarity_answer_generation

• Dataset: task494_review_polarity_answer_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 3 tokens mean: 107.47 tokens max: 256 tokens	min: 23 tokens mean: 113.5 tokens max: 256 tokens	min: 20 tokens mean: 115.05 tokens max: 249 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task670_ambigqa_question_generation

• Dataset: task670_ambigqa_question_generation
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 11 tokens mean: 12.69 tokens max: 26 tokens	min: 11 tokens mean: 12.49 tokens max: 23 tokens	min: 11 tokens mean: 12.21 tokens max: 18 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

task289_gigaword_summarization

• Dataset: task289_gigaword_summarization
• Size: 602 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 602 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 25 tokens mean: 51.33 tokens max: 87 tokens	min: 27 tokens mean: 51.71 tokens max: 87 tokens	min: 25 tokens mean: 51.16 tokens max: 87 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

npr

• Dataset: npr
• Size: 24,774 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 12.32 tokens max: 29 tokens	min: 17 tokens mean: 147.38 tokens max: 256 tokens	min: 11 tokens mean: 116.18 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

nli

• Dataset: nli
• Size: 49,516 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 3 tokens mean: 20.84 tokens max: 108 tokens	min: 3 tokens mean: 11.88 tokens max: 40 tokens	min: 3 tokens mean: 12.04 tokens max: 43 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

SimpleWiki

• Dataset: SimpleWiki
• Size: 4,974 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 8 tokens mean: 29.53 tokens max: 142 tokens	min: 8 tokens mean: 33.88 tokens max: 163 tokens	min: 10 tokens mean: 56.05 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

amazon_review_2018

• Dataset: amazon_review_2018
• Size: 99,000 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 11.33 tokens max: 33 tokens	min: 12 tokens mean: 90.13 tokens max: 256 tokens	min: 11 tokens mean: 70.75 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

ccnews_title_text

• Dataset: ccnews_title_text
• Size: 24,774 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 15.37 tokens max: 67 tokens	min: 21 tokens mean: 211.75 tokens max: 256 tokens	min: 21 tokens mean: 197.26 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

agnews

• Dataset: agnews
• Size: 44,574 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 11.9 tokens max: 62 tokens	min: 12 tokens mean: 40.68 tokens max: 256 tokens	min: 13 tokens mean: 45.78 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

xsum

• Dataset: xsum
• Size: 9,916 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 10 tokens mean: 28.01 tokens max: 64 tokens	min: 13 tokens mean: 227.16 tokens max: 256 tokens	min: 53 tokens mean: 232.64 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

msmarco

• Dataset: msmarco
• Size: 173,258 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 9.09 tokens max: 28 tokens	min: 19 tokens mean: 80.24 tokens max: 231 tokens	min: 24 tokens mean: 79.7 tokens max: 223 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

yahoo_answers_title_answer

• Dataset: yahoo_answers_title_answer
• Size: 24,774 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 16.64 tokens max: 43 tokens	min: 6 tokens mean: 81.11 tokens max: 256 tokens	min: 8 tokens mean: 84.22 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

squad_pairs

• Dataset: squad_pairs
• Size: 24,774 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 14.3 tokens max: 32 tokens	min: 27 tokens mean: 151.82 tokens max: 256 tokens	min: 27 tokens mean: 164.14 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

wow

• Dataset: wow
• Size: 29,716 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 87.98 tokens max: 256 tokens	min: 100 tokens mean: 111.37 tokens max: 141 tokens	min: 86 tokens mean: 113.24 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

mteb-amazon_counterfactual-avs_triplets

• Dataset: mteb-amazon_counterfactual-avs_triplets
• Size: 3,991 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 12 tokens mean: 27.55 tokens max: 137 tokens	min: 12 tokens mean: 26.96 tokens max: 137 tokens	min: 12 tokens mean: 26.59 tokens max: 137 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

mteb-amazon_massive_intent-avs_triplets

• Dataset: mteb-amazon_massive_intent-avs_triplets
• Size: 11,373 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 3 tokens mean: 9.47 tokens max: 30 tokens	min: 3 tokens mean: 9.05 tokens max: 32 tokens	min: 3 tokens mean: 9.4 tokens max: 30 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

mteb-amazon_massive_scenario-avs_triplets

• Dataset: mteb-amazon_massive_scenario-avs_triplets
• Size: 11,373 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 3 tokens mean: 9.56 tokens max: 32 tokens	min: 3 tokens mean: 9.13 tokens max: 30 tokens	min: 3 tokens mean: 9.61 tokens max: 32 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

mteb-amazon_reviews_multi-avs_triplets

• Dataset: mteb-amazon_reviews_multi-avs_triplets
• Size: 197,968 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 50.01 tokens max: 256 tokens	min: 7 tokens mean: 48.35 tokens max: 256 tokens	min: 7 tokens mean: 49.21 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

mteb-banking77-avs_triplets

• Dataset: mteb-banking77-avs_triplets
• Size: 9,915 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 5 tokens mean: 16.38 tokens max: 81 tokens	min: 5 tokens mean: 15.73 tokens max: 87 tokens	min: 4 tokens mean: 16.89 tokens max: 93 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

mteb-emotion-avs_triplets

• Dataset: mteb-emotion-avs_triplets
• Size: 15,840 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 22.57 tokens max: 67 tokens	min: 5 tokens mean: 17.39 tokens max: 78 tokens	min: 4 tokens mean: 21.81 tokens max: 68 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

mteb-imdb-avs_triplets

• Dataset: mteb-imdb-avs_triplets
• Size: 24,647 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 32 tokens mean: 206.04 tokens max: 256 tokens	min: 48 tokens mean: 225.35 tokens max: 256 tokens	min: 30 tokens mean: 207.14 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

mteb-mtop_domain-avs_triplets

• Dataset: mteb-mtop_domain-avs_triplets
• Size: 15,491 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 10.23 tokens max: 28 tokens	min: 4 tokens mean: 9.59 tokens max: 27 tokens	min: 4 tokens mean: 10.21 tokens max: 35 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

mteb-mtop_intent-avs_triplets

• Dataset: mteb-mtop_intent-avs_triplets
• Size: 15,491 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 10.1 tokens max: 28 tokens	min: 3 tokens mean: 9.74 tokens max: 26 tokens	min: 4 tokens mean: 10.14 tokens max: 34 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

mteb-toxic_conversations_50k-avs_triplets

• Dataset: mteb-toxic_conversations_50k-avs_triplets
• Size: 49,421 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 67.92 tokens max: 254 tokens	min: 3 tokens mean: 89.97 tokens max: 256 tokens	min: 4 tokens mean: 65.93 tokens max: 247 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

mteb-tweet_sentiment_extraction-avs_triplets

• Dataset: mteb-tweet_sentiment_extraction-avs_triplets
• Size: 27,213 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 3 tokens mean: 20.76 tokens max: 55 tokens	min: 3 tokens mean: 20.51 tokens max: 67 tokens	min: 3 tokens mean: 20.14 tokens max: 67 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

covid-bing-query-gpt4-avs_triplets

• Dataset: covid-bing-query-gpt4-avs_triplets
• Size: 4,942 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 7 tokens mean: 15.25 tokens max: 40 tokens	min: 18 tokens mean: 37.42 tokens max: 239 tokens	min: 18 tokens mean: 37.99 tokens max: 108 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

Evaluation Dataset

Unnamed Dataset

• Size: 18,269 evaluation samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 6 tokens mean: 15.98 tokens max: 64 tokens	min: 6 tokens mean: 143.49 tokens max: 256 tokens	min: 5 tokens mean: 143.76 tokens max: 256 tokens

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel 
    (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
  ), 'temperature': 0.01}
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 512
• learning_rate: 7.071067811865476e-06
• num_train_epochs: 80
• warmup_ratio: 0.1
• fp16: True
• hub_private_repo: True
• gradient_checkpointing: True
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 512
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 7.071067811865476e-06
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 80
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: True
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: True
• hub_always_push: False
• gradient_checkpointing: True
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Framework Versions

• Python: 3.10.10
• Sentence Transformers: 3.4.0.dev0
• Transformers: 4.46.3
• PyTorch: 2.5.1+cu124
• Accelerate: 0.34.2
• Datasets: 2.21.0
• Tokenizers: 0.20.4

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

GISTEmbedLoss

@misc{solatorio2024gistembed,
    title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
    author={Aivin V. Solatorio},
    year={2024},
    eprint={2402.16829},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}