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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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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: distilbert-base-uncased-finetuned-ner-cadec-active |
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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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# distilbert-base-uncased-finetuned-ner-cadec-active |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3768 |
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- Precision: 0.4818 |
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- Recall: 0.4749 |
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- F1: 0.4784 |
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- Accuracy: 0.8942 |
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- Adr Precision: 0.4034 |
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- Adr Recall: 0.4549 |
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- Adr F1: 0.4276 |
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- Disease Precision: 0.0 |
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- Disease Recall: 0.0 |
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- Disease F1: 0.0 |
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- Drug Precision: 0.8075 |
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- Drug Recall: 0.8032 |
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- Drug F1: 0.8053 |
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- Finding Precision: 0.0 |
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- Finding Recall: 0.0 |
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- Finding F1: 0.0 |
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- Symptom Precision: 0.0 |
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- Symptom Recall: 0.0 |
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- Symptom F1: 0.0 |
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- B-adr Precision: 0.6343 |
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- B-adr Recall: 0.5654 |
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- B-adr F1: 0.5978 |
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- B-disease Precision: 0.0 |
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- B-disease Recall: 0.0 |
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- B-disease F1: 0.0 |
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- B-drug Precision: 0.9632 |
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- B-drug Recall: 0.8351 |
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- B-drug F1: 0.8946 |
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- B-finding Precision: 0.0 |
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- B-finding Recall: 0.0 |
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- B-finding F1: 0.0 |
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- B-symptom Precision: 0.0 |
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- B-symptom Recall: 0.0 |
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- B-symptom F1: 0.0 |
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- I-adr Precision: 0.3549 |
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- I-adr Recall: 0.3932 |
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- I-adr F1: 0.3731 |
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- I-disease Precision: 0.0 |
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- I-disease Recall: 0.0 |
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- I-disease F1: 0.0 |
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- I-drug Precision: 0.8270 |
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- I-drug Recall: 0.8182 |
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- I-drug F1: 0.8226 |
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- I-finding Precision: 0.0 |
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- I-finding Recall: 0.0 |
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- I-finding F1: 0.0 |
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- I-symptom Precision: 0.0 |
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- I-symptom Recall: 0.0 |
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- I-symptom F1: 0.0 |
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- Macro Avg F1: 0.2688 |
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- Weighted Avg F1: 0.5224 |
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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: 8 |
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- eval_batch_size: 8 |
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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 | Adr Precision | Adr Recall | Adr F1 | Disease Precision | Disease Recall | Disease F1 | Drug Precision | Drug Recall | Drug F1 | Finding Precision | Finding Recall | Finding F1 | Symptom Precision | Symptom Recall | Symptom F1 | B-adr Precision | B-adr Recall | B-adr F1 | B-disease Precision | B-disease Recall | B-disease F1 | B-drug Precision | B-drug Recall | B-drug F1 | B-finding Precision | B-finding Recall | B-finding F1 | B-symptom Precision | B-symptom Recall | B-symptom F1 | I-adr Precision | I-adr Recall | I-adr F1 | I-disease Precision | I-disease Recall | I-disease F1 | I-drug Precision | I-drug Recall | I-drug F1 | I-finding Precision | I-finding Recall | I-finding F1 | I-symptom Precision | I-symptom Recall | I-symptom F1 | Macro Avg F1 | Weighted Avg F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|:------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------:|:-------:|:-----------------:|:--------------:|:----------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:-------------------:|:----------------:|:------------:|:----------------:|:-------------:|:---------:|:-------------------:|:----------------:|:------------:|:-------------------:|:----------------:|:------------:|:---------------:|:------------:|:--------:|:-------------------:|:----------------:|:------------:|:----------------:|:-------------:|:---------:|:-------------------:|:----------------:|:------------:|:-------------------:|:----------------:|:------------:|:------------:|:---------------:| |
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| No log | 1.0 | 19 | 0.8076 | 0.0 | 0.0 | 0.0 | 0.7876 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
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| No log | 2.0 | 38 | 0.5384 | 0.1984 | 0.1044 | 0.1368 | 0.8425 | 0.2081 | 0.1192 | 0.1516 | 0.0 | 0.0 | 0.0 | 0.1667 | 0.1064 | 0.1299 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9833 | 0.6277 | 0.7662 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0736 | 0.0521 | 0.0610 | 0.0 | 0.0 | 0.0 | 1.0 | 0.1070 | 0.1932 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1020 | 0.1230 | |
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| No log | 3.0 | 57 | 0.4591 | 0.4023 | 0.2866 | 0.3347 | 0.8624 | 0.2761 | 0.2267 | 0.2490 | 0.0 | 0.0 | 0.0 | 0.9466 | 0.6596 | 0.7774 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5625 | 0.0992 | 0.1687 | 0.0 | 0.0 | 0.0 | 0.9846 | 0.6809 | 0.8050 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1178 | 0.1131 | 0.1154 | 0.0 | 0.0 | 0.0 | 0.9690 | 0.6684 | 0.7911 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1880 | 0.2703 | |
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| No log | 4.0 | 76 | 0.4170 | 0.3997 | 0.3183 | 0.3544 | 0.8616 | 0.2945 | 0.2645 | 0.2787 | 0.0 | 0.0 | 0.0 | 0.8063 | 0.6862 | 0.7414 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6596 | 0.0488 | 0.0909 | 0.0 | 0.0 | 0.0 | 0.9592 | 0.75 | 0.8418 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0786 | 0.0862 | 0.0822 | 0.0 | 0.0 | 0.0 | 0.8397 | 0.7005 | 0.7638 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1779 | 0.2324 | |
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| No log | 5.0 | 95 | 0.3934 | 0.4318 | 0.3756 | 0.4018 | 0.8761 | 0.3328 | 0.3241 | 0.3284 | 0.0 | 0.0 | 0.0 | 0.8 | 0.7660 | 0.7826 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6260 | 0.2583 | 0.3657 | 0.0 | 0.0 | 0.0 | 0.9551 | 0.7926 | 0.8663 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1842 | 0.2011 | 0.1923 | 0.0 | 0.0 | 0.0 | 0.8371 | 0.7968 | 0.8164 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2241 | 0.3761 | |
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| No log | 6.0 | 114 | 0.3851 | 0.4452 | 0.3992 | 0.4209 | 0.8863 | 0.3453 | 0.3503 | 0.3478 | 0.0 | 0.0 | 0.0 | 0.8371 | 0.7926 | 0.8142 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6438 | 0.3701 | 0.47 | 0.0 | 0.0 | 0.0 | 0.9745 | 0.8138 | 0.8870 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2438 | 0.2639 | 0.2534 | 0.0 | 0.0 | 0.0 | 0.8531 | 0.8075 | 0.8297 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2440 | 0.4374 | |
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| No log | 7.0 | 133 | 0.3812 | 0.4546 | 0.4309 | 0.4425 | 0.8906 | 0.3636 | 0.3953 | 0.3788 | 0.0 | 0.0 | 0.0 | 0.8371 | 0.7926 | 0.8142 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6225 | 0.4961 | 0.5521 | 0.0 | 0.0 | 0.0 | 0.9745 | 0.8138 | 0.8870 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3067 | 0.3303 | 0.3181 | 0.0 | 0.0 | 0.0 | 0.8580 | 0.8075 | 0.8320 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2589 | 0.4883 | |
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| No log | 8.0 | 152 | 0.3768 | 0.4640 | 0.4483 | 0.4560 | 0.8927 | 0.3804 | 0.4186 | 0.3986 | 0.0 | 0.0 | 0.0 | 0.8021 | 0.7979 | 0.8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6374 | 0.5150 | 0.5697 | 0.0 | 0.0 | 0.0 | 0.9630 | 0.8298 | 0.8914 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3279 | 0.3609 | 0.3436 | 0.0 | 0.0 | 0.0 | 0.8216 | 0.8128 | 0.8172 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2622 | 0.5017 | |
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| No log | 9.0 | 171 | 0.3767 | 0.4615 | 0.4534 | 0.4574 | 0.8904 | 0.3787 | 0.4244 | 0.4003 | 0.0 | 0.0 | 0.0 | 0.7989 | 0.8032 | 0.8011 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6273 | 0.5433 | 0.5823 | 0.0 | 0.0 | 0.0 | 0.9632 | 0.8351 | 0.8946 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3285 | 0.3662 | 0.3463 | 0.0 | 0.0 | 0.0 | 0.8191 | 0.8235 | 0.8213 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2645 | 0.5080 | |
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| No log | 10.0 | 190 | 0.3768 | 0.4818 | 0.4749 | 0.4784 | 0.8942 | 0.4034 | 0.4549 | 0.4276 | 0.0 | 0.0 | 0.0 | 0.8075 | 0.8032 | 0.8053 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6343 | 0.5654 | 0.5978 | 0.0 | 0.0 | 0.0 | 0.9632 | 0.8351 | 0.8946 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3549 | 0.3932 | 0.3731 | 0.0 | 0.0 | 0.0 | 0.8270 | 0.8182 | 0.8226 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2688 | 0.5224 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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