slurp-slot_baseline-xlm_r-en
This model is a fine-tuned version of xlm-roberta-base on the SLURP dataset.
It achieves the following results on the test set:
- Loss: 0.3263
- Precision: 0.7954
- Recall: 0.8413
- F1: 0.8177
- Accuracy: 0.9268
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 1.1437 | 1.0 | 720 | 0.5236 | 0.6852 | 0.6623 | 0.6736 | 0.8860 |
| 0.5761 | 2.0 | 1440 | 0.3668 | 0.7348 | 0.7829 | 0.7581 | 0.9119 |
| 0.3087 | 3.0 | 2160 | 0.2996 | 0.7925 | 0.8280 | 0.8099 | 0.9270 |
| 0.2631 | 4.0 | 2880 | 0.2959 | 0.7872 | 0.8487 | 0.8168 | 0.9275 |
| 0.1847 | 5.0 | 3600 | 0.3121 | 0.7929 | 0.8373 | 0.8145 | 0.9290 |
| 0.1518 | 6.0 | 4320 | 0.3117 | 0.8080 | 0.8601 | 0.8332 | 0.9329 |
| 0.1232 | 7.0 | 5040 | 0.3153 | 0.7961 | 0.8490 | 0.8217 | 0.9267 |
| 0.0994 | 8.0 | 5760 | 0.3125 | 0.8105 | 0.8570 | 0.8331 | 0.9332 |
| 0.0968 | 9.0 | 6480 | 0.3242 | 0.8147 | 0.8637 | 0.8385 | 0.9329 |
| 0.0772 | 10.0 | 7200 | 0.3263 | 0.8145 | 0.8641 | 0.8386 | 0.9341 |
Test results per slot
| slot | f1 | tc_size |
|---|---|---|
| alarm_type | 0.4 | 4 |
| app_name | 0.42857142857142855 | 10 |
| artist_name | 0.8122605363984675 | 123 |
| audiobook_author | 0.0 | 9 |
| audiobook_name | 0.6021505376344087 | 43 |
| business_name | 0.8530259365994236 | 184 |
| business_type | 0.6666666666666667 | 41 |
| change_amount | 0.6666666666666666 | 9 |
| coffee_type | 0.5333333333333333 | 6 |
| color_type | 0.8135593220338982 | 28 |
| cooking_type | 0.8333333333333333 | 14 |
| currency_name | 0.8611111111111112 | 70 |
| date | 0.9034267912772587 | 623 |
| definition_word | 0.88 | 97 |
| device_type | 0.8053691275167785 | 71 |
| drink_type | 0.0 | 2 |
| email_address | 0.9599999999999999 | 38 |
| email_folder | 0.9523809523809523 | 10 |
| event_name | 0.7643504531722054 | 321 |
| food_type | 0.7482014388489208 | 121 |
| game_name | 0.7789473684210527 | 44 |
| general_frequency | 0.5862068965517242 | 21 |
| house_place | 0.8840579710144928 | 68 |
| ingredient | 0.0 | 13 |
| joke_type | 0.9411764705882353 | 17 |
| list_name | 0.7979274611398963 | 91 |
| meal_type | 0.782608695652174 | 18 |
| media_type | 0.8596491228070176 | 173 |
| movie_name | 0.0 | 3 |
| movie_type | 0.5 | 3 |
| music_album | 0.0 | 2 |
| music_descriptor | 0.25 | 8 |
| music_genre | 0.7244094488188977 | 58 |
| news_topic | 0.5675675675675675 | 64 |
| order_type | 0.7941176470588235 | 29 |
| person | 0.9128094725511302 | 438 |
| personal_info | 0.6666666666666666 | 16 |
| place_name | 0.8725790010193679 | 493 |
| player_setting | 0.5405405405405405 | 42 |
| playlist_name | 0.5 | 27 |
| podcast_descriptor | 0.4888888888888888 | 28 |
| podcast_name | 0.5245901639344263 | 31 |
| radio_name | 0.6504065040650406 | 53 |
| relation | 0.8478260869565218 | 87 |
| song_name | 0.7058823529411765 | 54 |
| time | 0.7914893617021276 | 236 |
| time_zone | 0.7804878048780488 | 23 |
| timeofday | 0.8396946564885496 | 60 |
| transport_agency | 0.8571428571428571 | 18 |
| transport_descriptor | 0.0 | 2 |
| transport_name | 0.4 | 7 |
| transport_type | 0.9481481481481482 | 68 |
| weather_descriptor | 0.789272030651341 | 123 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3