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license: apache-2.0 |
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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: bert-medical-ner |
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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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# bert-medical-ner |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0001 |
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- Precision: 0.6207 |
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- Recall: 0.6501 |
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- F1: 0.6351 |
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- Accuracy: 0.7695 |
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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: 64 |
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- eval_batch_size: 64 |
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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: 20 |
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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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| No log | 1.0 | 63 | 1.5798 | 0.3498 | 0.3921 | 0.3697 | 0.6168 | |
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| No log | 2.0 | 126 | 1.1942 | 0.5020 | 0.5286 | 0.5150 | 0.7028 | |
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| No log | 3.0 | 189 | 1.0593 | 0.5345 | 0.5826 | 0.5575 | 0.7280 | |
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| No log | 4.0 | 252 | 0.9799 | 0.5722 | 0.6065 | 0.5889 | 0.7451 | |
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| No log | 5.0 | 315 | 0.9394 | 0.5905 | 0.6187 | 0.6043 | 0.7534 | |
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| No log | 6.0 | 378 | 0.9171 | 0.5995 | 0.6262 | 0.6126 | 0.7576 | |
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| No log | 7.0 | 441 | 0.9068 | 0.6071 | 0.6324 | 0.6195 | 0.7623 | |
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| 1.0968 | 8.0 | 504 | 0.9076 | 0.6171 | 0.6323 | 0.6246 | 0.7638 | |
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| 1.0968 | 9.0 | 567 | 0.9280 | 0.6095 | 0.6361 | 0.6225 | 0.7637 | |
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| 1.0968 | 10.0 | 630 | 0.9231 | 0.6117 | 0.6414 | 0.6262 | 0.7670 | |
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| 1.0968 | 11.0 | 693 | 0.9322 | 0.6183 | 0.6460 | 0.6319 | 0.7685 | |
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| 1.0968 | 12.0 | 756 | 0.9529 | 0.6200 | 0.6503 | 0.6347 | 0.7689 | |
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| 1.0968 | 13.0 | 819 | 0.9550 | 0.6148 | 0.6451 | 0.6296 | 0.7672 | |
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| 1.0968 | 14.0 | 882 | 0.9736 | 0.6227 | 0.6466 | 0.6344 | 0.7688 | |
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| 1.0968 | 15.0 | 945 | 0.9791 | 0.6206 | 0.6460 | 0.6330 | 0.7679 | |
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| 0.4223 | 16.0 | 1008 | 0.9854 | 0.6194 | 0.6490 | 0.6339 | 0.7699 | |
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| 0.4223 | 17.0 | 1071 | 0.9870 | 0.6185 | 0.6494 | 0.6336 | 0.7692 | |
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| 0.4223 | 18.0 | 1134 | 0.9957 | 0.6208 | 0.6498 | 0.6350 | 0.7702 | |
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| 0.4223 | 19.0 | 1197 | 0.9994 | 0.6189 | 0.6510 | 0.6345 | 0.7693 | |
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| 0.4223 | 20.0 | 1260 | 1.0001 | 0.6207 | 0.6501 | 0.6351 | 0.7695 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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