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