Training complete
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README.md
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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-no-iob
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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-no-iob
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4152
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- Precision: 0.5826
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- Recall: 0.6187
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- F1: 0.6001
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- Accuracy: 0.9303
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- Adr Precision: 0.5109
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- Adr Recall: 0.5773
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- Adr F1: 0.5421
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- Disease Precision: 0.4643
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- Disease Recall: 0.4062
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- Disease F1: 0.4333
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- Drug Precision: 0.8743
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- Drug Recall: 0.8889
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- Drug F1: 0.8815
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- Finding Precision: 0.2143
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- Finding Recall: 0.1875
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- Finding F1: 0.2000
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- Symptom Precision: 0.5556
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- Symptom Recall: 0.3448
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- Symptom F1: 0.4255
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- Macro Avg F1: 0.4965
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- Weighted Avg F1: 0.5992
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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: 40
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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 | Macro Avg F1 | Weighted Avg F1 |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|:------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------:|:-------:|:-----------------:|:--------------:|:----------:|:-----------------:|:--------------:|:----------:|:------------:|:---------------:|
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| No log | 1.0 | 125 | 0.2402 | 0.4952 | 0.5462 | 0.5194 | 0.9140 | 0.3893 | 0.5258 | 0.4474 | 0.0 | 0.0 | 0.0 | 0.8883 | 0.8833 | 0.8858 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2666 | 0.4966 |
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| No log | 2.0 | 250 | 0.2136 | 0.5380 | 0.5976 | 0.5663 | 0.9239 | 0.4412 | 0.5649 | 0.4955 | 0.6818 | 0.4688 | 0.5556 | 0.8503 | 0.8833 | 0.8665 | 0.2857 | 0.0625 | 0.1026 | 0.6 | 0.1034 | 0.1765 | 0.4393 | 0.5573 |
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| No log | 3.0 | 375 | 0.2199 | 0.5283 | 0.5660 | 0.5465 | 0.9191 | 0.4492 | 0.5010 | 0.4737 | 0.5455 | 0.375 | 0.4444 | 0.8674 | 0.8722 | 0.8698 | 0.1515 | 0.1562 | 0.1538 | 0.3429 | 0.4138 | 0.375 | 0.4634 | 0.5492 |
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| 0.2232 | 4.0 | 500 | 0.2292 | 0.5622 | 0.5726 | 0.5673 | 0.9228 | 0.4971 | 0.5278 | 0.512 | 0.0 | 0.0 | 0.0 | 0.8791 | 0.8889 | 0.8840 | 0.1852 | 0.3125 | 0.2326 | 0.4211 | 0.2759 | 0.3333 | 0.3924 | 0.5601 |
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| 0.2232 | 5.0 | 625 | 0.2474 | 0.5863 | 0.6095 | 0.5977 | 0.9265 | 0.5055 | 0.5732 | 0.5372 | 0.6 | 0.375 | 0.4615 | 0.8785 | 0.8833 | 0.8809 | 0.2273 | 0.1562 | 0.1852 | 0.5333 | 0.2759 | 0.3636 | 0.4857 | 0.5941 |
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| 0.2232 | 6.0 | 750 | 0.2474 | 0.5635 | 0.5910 | 0.5769 | 0.9244 | 0.4842 | 0.5381 | 0.5098 | 0.375 | 0.375 | 0.375 | 0.8840 | 0.8889 | 0.8864 | 0.16 | 0.125 | 0.1404 | 0.6111 | 0.3793 | 0.4681 | 0.4759 | 0.5763 |
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| 0.2232 | 7.0 | 875 | 0.2709 | 0.5758 | 0.5963 | 0.5859 | 0.9275 | 0.4991 | 0.5423 | 0.5198 | 0.5 | 0.2812 | 0.36 | 0.8710 | 0.9 | 0.8852 | 0.2683 | 0.3438 | 0.3014 | 0.5385 | 0.2414 | 0.3333 | 0.4799 | 0.5835 |
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| 0.0707 | 8.0 | 1000 | 0.2611 | 0.5752 | 0.6003 | 0.5875 | 0.9282 | 0.4991 | 0.5485 | 0.5226 | 0.6923 | 0.2812 | 0.4 | 0.8689 | 0.8833 | 0.8760 | 0.2895 | 0.3438 | 0.3143 | 0.4167 | 0.3448 | 0.3774 | 0.4981 | 0.5870 |
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| 0.0707 | 9.0 | 1125 | 0.2664 | 0.5749 | 0.6227 | 0.5978 | 0.9297 | 0.5111 | 0.5711 | 0.5394 | 0.4375 | 0.4375 | 0.4375 | 0.8710 | 0.9 | 0.8852 | 0.2093 | 0.2812 | 0.24 | 0.5556 | 0.3448 | 0.4255 | 0.5055 | 0.6003 |
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| 0.0707 | 10.0 | 1250 | 0.3066 | 0.5537 | 0.5778 | 0.5655 | 0.9268 | 0.4761 | 0.5134 | 0.4940 | 0.4545 | 0.3125 | 0.3704 | 0.8610 | 0.8944 | 0.8774 | 0.25 | 0.2812 | 0.2647 | 0.3913 | 0.3103 | 0.3462 | 0.4705 | 0.5645 |
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| 0.0707 | 11.0 | 1375 | 0.2980 | 0.5751 | 0.5910 | 0.5830 | 0.9282 | 0.4971 | 0.5340 | 0.5149 | 0.4615 | 0.375 | 0.4138 | 0.8602 | 0.8889 | 0.8743 | 0.2917 | 0.2188 | 0.25 | 0.4545 | 0.3448 | 0.3922 | 0.4890 | 0.5801 |
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| 0.0293 | 12.0 | 1500 | 0.3272 | 0.5932 | 0.6174 | 0.6050 | 0.9303 | 0.5082 | 0.5732 | 0.5388 | 0.6316 | 0.375 | 0.4706 | 0.8901 | 0.9 | 0.8950 | 0.2609 | 0.1875 | 0.2182 | 0.5556 | 0.3448 | 0.4255 | 0.5096 | 0.6026 |
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| 87 |
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| 0.0293 | 13.0 | 1625 | 0.3161 | 0.5664 | 0.6187 | 0.5914 | 0.9288 | 0.4937 | 0.5691 | 0.5287 | 0.3846 | 0.4688 | 0.4225 | 0.8804 | 0.9 | 0.8901 | 0.2308 | 0.1875 | 0.2069 | 0.5 | 0.3448 | 0.4082 | 0.4913 | 0.5919 |
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| 88 |
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| 0.0293 | 14.0 | 1750 | 0.3529 | 0.5736 | 0.6016 | 0.5873 | 0.9269 | 0.4806 | 0.5361 | 0.5068 | 0.5652 | 0.4062 | 0.4727 | 0.8913 | 0.9111 | 0.9011 | 0.3077 | 0.25 | 0.2759 | 0.5238 | 0.3793 | 0.44 | 0.5193 | 0.5867 |
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| 89 |
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| 0.0293 | 15.0 | 1875 | 0.3381 | 0.5608 | 0.6082 | 0.5835 | 0.9290 | 0.5074 | 0.5649 | 0.5346 | 0.2857 | 0.1875 | 0.2264 | 0.8757 | 0.9 | 0.8877 | 0.1731 | 0.2812 | 0.2143 | 0.4167 | 0.3448 | 0.3774 | 0.4481 | 0.5859 |
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| 90 |
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| 0.0133 | 16.0 | 2000 | 0.3275 | 0.5833 | 0.6187 | 0.6005 | 0.9307 | 0.5064 | 0.5711 | 0.5368 | 0.4286 | 0.375 | 0.4000 | 0.8852 | 0.9 | 0.8926 | 0.2759 | 0.25 | 0.2623 | 0.5882 | 0.3448 | 0.4348 | 0.5053 | 0.6000 |
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| 91 |
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| 0.0133 | 17.0 | 2125 | 0.3623 | 0.5787 | 0.6161 | 0.5968 | 0.9310 | 0.4928 | 0.5649 | 0.5264 | 0.6 | 0.4688 | 0.5263 | 0.8852 | 0.9 | 0.8926 | 0.24 | 0.1875 | 0.2105 | 0.5556 | 0.3448 | 0.4255 | 0.5163 | 0.5962 |
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| 92 |
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| 0.0133 | 18.0 | 2250 | 0.3466 | 0.5699 | 0.6187 | 0.5933 | 0.9299 | 0.4937 | 0.5691 | 0.5287 | 0.3889 | 0.4375 | 0.4118 | 0.8901 | 0.9 | 0.8950 | 0.25 | 0.2188 | 0.2333 | 0.5556 | 0.3448 | 0.4255 | 0.4989 | 0.5944 |
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| 93 |
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| 0.0133 | 19.0 | 2375 | 0.3496 | 0.5751 | 0.6214 | 0.5973 | 0.9321 | 0.5101 | 0.5753 | 0.5407 | 0.4 | 0.375 | 0.3871 | 0.8798 | 0.8944 | 0.8871 | 0.1860 | 0.25 | 0.2133 | 0.6875 | 0.3793 | 0.4889 | 0.5034 | 0.6007 |
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| 0.0075 | 20.0 | 2500 | 0.3676 | 0.5898 | 0.6280 | 0.6083 | 0.9314 | 0.5090 | 0.5814 | 0.5428 | 0.5185 | 0.4375 | 0.4746 | 0.8852 | 0.9 | 0.8926 | 0.2692 | 0.2188 | 0.2414 | 0.6471 | 0.3793 | 0.4783 | 0.5259 | 0.6078 |
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| 95 |
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| 0.0075 | 21.0 | 2625 | 0.3658 | 0.5816 | 0.6253 | 0.6027 | 0.9306 | 0.4991 | 0.5753 | 0.5345 | 0.5185 | 0.4375 | 0.4746 | 0.8811 | 0.9056 | 0.8932 | 0.2593 | 0.2188 | 0.2373 | 0.6471 | 0.3793 | 0.4783 | 0.5236 | 0.6024 |
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| 96 |
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| 0.0075 | 22.0 | 2750 | 0.3803 | 0.5804 | 0.6187 | 0.5990 | 0.9294 | 0.5148 | 0.5753 | 0.5433 | 0.3846 | 0.3125 | 0.3448 | 0.8859 | 0.9056 | 0.8956 | 0.2059 | 0.2188 | 0.2121 | 0.4545 | 0.3448 | 0.3922 | 0.4776 | 0.5988 |
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| 97 |
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| 0.0075 | 23.0 | 2875 | 0.3795 | 0.5954 | 0.6174 | 0.6062 | 0.9305 | 0.5139 | 0.5711 | 0.5410 | 0.5652 | 0.4062 | 0.4727 | 0.8852 | 0.9 | 0.8926 | 0.2609 | 0.1875 | 0.2182 | 0.5556 | 0.3448 | 0.4255 | 0.5100 | 0.6036 |
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| 98 |
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| 0.0051 | 24.0 | 3000 | 0.3849 | 0.5774 | 0.6148 | 0.5955 | 0.9295 | 0.5093 | 0.5670 | 0.5366 | 0.4444 | 0.375 | 0.4068 | 0.8798 | 0.8944 | 0.8871 | 0.2121 | 0.2188 | 0.2154 | 0.4583 | 0.3793 | 0.4151 | 0.4922 | 0.5961 |
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| 99 |
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| 0.0051 | 25.0 | 3125 | 0.3847 | 0.5911 | 0.6293 | 0.6096 | 0.9303 | 0.5247 | 0.5918 | 0.5562 | 0.4828 | 0.4375 | 0.4590 | 0.875 | 0.8944 | 0.8846 | 0.1724 | 0.1562 | 0.1639 | 0.5556 | 0.3448 | 0.4255 | 0.4979 | 0.6085 |
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| 100 |
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| 0.0051 | 26.0 | 3250 | 0.3917 | 0.5901 | 0.6266 | 0.6078 | 0.9298 | 0.5165 | 0.5794 | 0.5462 | 0.4667 | 0.4375 | 0.4516 | 0.8804 | 0.9 | 0.8901 | 0.2759 | 0.25 | 0.2623 | 0.5556 | 0.3448 | 0.4255 | 0.5151 | 0.6072 |
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| 101 |
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| 0.0051 | 27.0 | 3375 | 0.3915 | 0.5901 | 0.6306 | 0.6097 | 0.9306 | 0.5182 | 0.5876 | 0.5507 | 0.4828 | 0.4375 | 0.4590 | 0.8852 | 0.9 | 0.8926 | 0.2414 | 0.2188 | 0.2295 | 0.5263 | 0.3448 | 0.4167 | 0.5097 | 0.6093 |
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| 102 |
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| 0.0034 | 28.0 | 3500 | 0.4010 | 0.5881 | 0.6253 | 0.6061 | 0.9305 | 0.5240 | 0.5856 | 0.5531 | 0.4167 | 0.3125 | 0.3571 | 0.8757 | 0.9 | 0.8877 | 0.2162 | 0.25 | 0.2319 | 0.5556 | 0.3448 | 0.4255 | 0.4911 | 0.6058 |
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| 103 |
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| 0.0034 | 29.0 | 3625 | 0.4136 | 0.5955 | 0.6293 | 0.6119 | 0.9313 | 0.5212 | 0.5835 | 0.5506 | 0.4828 | 0.4375 | 0.4590 | 0.8859 | 0.9056 | 0.8956 | 0.2692 | 0.2188 | 0.2414 | 0.5263 | 0.3448 | 0.4167 | 0.5127 | 0.6105 |
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| 104 |
+
| 0.0034 | 30.0 | 3750 | 0.4072 | 0.5918 | 0.6293 | 0.6100 | 0.9312 | 0.5191 | 0.5876 | 0.5513 | 0.4615 | 0.375 | 0.4138 | 0.8804 | 0.9 | 0.8901 | 0.2581 | 0.25 | 0.2540 | 0.625 | 0.3448 | 0.4444 | 0.5107 | 0.6093 |
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| 105 |
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| 0.0034 | 31.0 | 3875 | 0.4081 | 0.5995 | 0.6240 | 0.6115 | 0.9307 | 0.5294 | 0.5753 | 0.5514 | 0.4375 | 0.4375 | 0.4375 | 0.8804 | 0.9 | 0.8901 | 0.32 | 0.25 | 0.2807 | 0.4762 | 0.3448 | 0.4000 | 0.5119 | 0.6098 |
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| 106 |
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| 0.0025 | 32.0 | 4000 | 0.4022 | 0.5885 | 0.6319 | 0.6094 | 0.9312 | 0.5152 | 0.5938 | 0.5517 | 0.5185 | 0.4375 | 0.4746 | 0.875 | 0.8944 | 0.8846 | 0.25 | 0.1875 | 0.2143 | 0.5 | 0.3448 | 0.4082 | 0.5067 | 0.6078 |
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| 107 |
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| 0.0025 | 33.0 | 4125 | 0.4066 | 0.5821 | 0.6266 | 0.6036 | 0.9312 | 0.5108 | 0.5876 | 0.5465 | 0.4643 | 0.4062 | 0.4333 | 0.8743 | 0.8889 | 0.8815 | 0.2414 | 0.2188 | 0.2295 | 0.5556 | 0.3448 | 0.4255 | 0.5033 | 0.6033 |
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| 108 |
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| 0.0025 | 34.0 | 4250 | 0.4049 | 0.5865 | 0.6306 | 0.6078 | 0.9318 | 0.5198 | 0.5959 | 0.5552 | 0.4815 | 0.4062 | 0.4407 | 0.8696 | 0.8889 | 0.8791 | 0.2 | 0.1875 | 0.1935 | 0.5556 | 0.3448 | 0.4255 | 0.4988 | 0.6071 |
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| 109 |
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| 0.0025 | 35.0 | 4375 | 0.4129 | 0.5741 | 0.6187 | 0.5956 | 0.9294 | 0.5009 | 0.5773 | 0.5364 | 0.5 | 0.4375 | 0.4667 | 0.8689 | 0.8833 | 0.8760 | 0.2069 | 0.1875 | 0.1967 | 0.5556 | 0.3448 | 0.4255 | 0.5003 | 0.5955 |
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| 110 |
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| 0.002 | 36.0 | 4500 | 0.4134 | 0.5843 | 0.6266 | 0.6047 | 0.9303 | 0.5117 | 0.5876 | 0.5470 | 0.5 | 0.4375 | 0.4667 | 0.8743 | 0.8889 | 0.8815 | 0.2222 | 0.1875 | 0.2034 | 0.5556 | 0.3448 | 0.4255 | 0.5048 | 0.6039 |
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| 111 |
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| 0.002 | 37.0 | 4625 | 0.4138 | 0.5828 | 0.6266 | 0.6039 | 0.9303 | 0.5099 | 0.5856 | 0.5451 | 0.4815 | 0.4062 | 0.4407 | 0.875 | 0.8944 | 0.8846 | 0.2414 | 0.2188 | 0.2295 | 0.5556 | 0.3448 | 0.4255 | 0.5051 | 0.6034 |
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| 112 |
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| 0.002 | 38.0 | 4750 | 0.4126 | 0.5804 | 0.6187 | 0.5990 | 0.9297 | 0.5100 | 0.5794 | 0.5425 | 0.4444 | 0.375 | 0.4068 | 0.8743 | 0.8889 | 0.8815 | 0.2069 | 0.1875 | 0.1967 | 0.5556 | 0.3448 | 0.4255 | 0.4906 | 0.5982 |
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| 113 |
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| 0.002 | 39.0 | 4875 | 0.4139 | 0.5797 | 0.6187 | 0.5986 | 0.9301 | 0.5118 | 0.5794 | 0.5435 | 0.4286 | 0.375 | 0.4000 | 0.8743 | 0.8889 | 0.8815 | 0.1935 | 0.1875 | 0.1905 | 0.5556 | 0.3448 | 0.4255 | 0.4882 | 0.5983 |
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| 114 |
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| 0.0017 | 40.0 | 5000 | 0.4152 | 0.5826 | 0.6187 | 0.6001 | 0.9303 | 0.5109 | 0.5773 | 0.5421 | 0.4643 | 0.4062 | 0.4333 | 0.8743 | 0.8889 | 0.8815 | 0.2143 | 0.1875 | 0.2000 | 0.5556 | 0.3448 | 0.4255 | 0.4965 | 0.5992 |
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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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| 121 |
+
- Datasets 2.15.0
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| 122 |
+
- Tokenizers 0.15.0
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