all-MiniLM-L6-v43-pair_score

This is a sentence-transformers model finetuned from KhaledReda/all-MiniLM-L6-v36-pair_score on the pairs_with_scores_v35 dataset. It maps sentences & paragraphs to a 384-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: KhaledReda/all-MiniLM-L6-v36-pair_score
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • pairs_with_scores_v35
• 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, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'invisible antiprespirant',
    'mc sauce buffalo wings sauce - 325 gr, sauce buffalo sauce buffalo wings sauce mc sauce mc sauce buffalo sauce mc sauce wings sauce wings sauce, buffalo wings sauce mc sauce mc sauce buffalo sauce mc sauce wings sauce wings sauce, spice up your favorite wings and enjoy a tasty appetizer with this buffalo wings sauce from mc sauce and indulge in the flavorful taste.',
    'light glass wall, wall light for bedroom wall mounted light fixture led wall light smoked gray glass wall light bedroom wall light glass wall lights indoor wall sconces modern wall lights wall lamps wall lighting, glass wall lights indoor wall sconces modern wall lights wall lamps wall lighting, high lighting generic wall light type of decor style modern 15 l 20 w 20 h light type led number of lights 1 sphere glass black, 1 light glass wall mounted light / fixture modern sphere wall light for bedroom. type led glass smoked gray dimensions 15 l 20 w 20 h',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000, -0.1870, -0.1954],
#         [-0.1870,  1.0000,  0.1491],
#         [-0.1954,  0.1491,  1.0000]])

Training Details

Training Dataset

pairs_with_scores_v35

• Dataset: pairs_with_scores_v35 at 2b1d182
• Size: 19,761,536 training samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 3 tokens mean: 5.37 tokens max: 20 tokens	min: 4 tokens mean: 77.84 tokens max: 256 tokens	min: 0.0 mean: 0.05 max: 1.0

• Samples:

  sentence1	sentence2	score
  dettol	ebullient top, lightweight top fabric top summer top polyester top lycra top women top shirts tops ebullient top top, ebullient top top, gender women bardees designs generic top ebullient one features lightweight polyester lycra off white season summer, introducing our ebullient top - the ultimate must-have for fashion-forward minimalists made with a lightweight fabric this not-so-basic basic shirt is perfect for staying cool and stylish this summer. elevate your wardrobe with this versatile and comfortable piece. 97 polyester 3 lycra	0.0
  dairy	blue collar for small dogs, blue collar pet collar collar dogs collar, collar dogs collar, small	0.0
  nyc linen	tryptizol - 25 mg amitriptyline hydrochloride - 30 tablets, tryptizol tryptizol amitriptyline hydrochloride tryptizol tablets, pharmacies form tablets units 0.025 gram	0.0

• Loss: CoSENTLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

Evaluation Dataset

pairs_with_scores_v35

• Dataset: pairs_with_scores_v35 at 2b1d182
• Size: 99,305 evaluation samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 3 tokens mean: 5.38 tokens max: 19 tokens	min: 10 tokens mean: 80.58 tokens max: 256 tokens	min: 0.0 mean: 0.04 max: 1.0

• Samples:

  sentence1	sentence2	score
  pretend play	pw 540 flex-h women s fitness walking shoes - black, comfortable walking shoe dynamic fitness shoe flexible walking shoe women shoe black shoe running and walking shoes, shoes, gender women decathlon generic shoes 2.5 35 black activity fitness target group women sport fitness walking, alix a product engineer developed this shoe for your regular fitness walking outings. the even more comfortable more dynamic and more flexible flex-h technology conforms to the requirements of your feet	0.0
  lemon fresh soap bar	bed runner table runner off white, off white runner handwoven runner bed runner runner table runner, bed runner runner table runner, inches home generic runner 90 cm 220 cm 1 rectangular decorative bed runner features handwoven white, handwoven runner could be used for a dining table or bed. product specifications color off white. material handwoven.	0.0
  iphone 13 - midnight 128gb	verde blazer set, women blazer outfit women outfit green outfit blazer outfit outfit set verde blazer verde blazer outfit, blazer outfit outfit set verde blazer verde blazer outfit, gender women age	0.0

• Loss: CoSENTLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• learning_rate: 2e-05
• num_train_epochs: 1
• warmup_ratio: 0.1
• fp16: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• 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: 2e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 1
• 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: None
• hub_always_push: False
• hub_revision: None
• gradient_checkpointing: False
• 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
• 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
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Click to expand

Framework Versions

• Python: 3.12.3
• Sentence Transformers: 5.1.0
• Transformers: 4.55.4
• PyTorch: 2.6.0+cu124
• Accelerate: 1.10.1
• Datasets: 4.0.0
• Tokenizers: 0.21.4

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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}