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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: 0.4000 |
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- Precision: 0.7565 |
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- Recall: 0.6791 |
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- F1: 0.7157 |
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- Accuracy: 0.9405 |
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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 | 0.4610 | 0.6626 | 0.3672 | 0.4726 | 0.9098 | |
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| No log | 2.0 | 126 | 0.3464 | 0.6908 | 0.5113 | 0.5877 | 0.9245 | |
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| No log | 3.0 | 189 | 0.3237 | 0.6658 | 0.5898 | 0.6255 | 0.9268 | |
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| No log | 4.0 | 252 | 0.3029 | 0.6965 | 0.6147 | 0.6531 | 0.9322 | |
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| No log | 5.0 | 315 | 0.3327 | 0.7542 | 0.6102 | 0.6746 | 0.9341 | |
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| No log | 6.0 | 378 | 0.3239 | 0.7371 | 0.6305 | 0.6797 | 0.9364 | |
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| No log | 7.0 | 441 | 0.3318 | 0.6975 | 0.6825 | 0.6899 | 0.9353 | |
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| 0.2658 | 8.0 | 504 | 0.3478 | 0.7440 | 0.6667 | 0.7032 | 0.9380 | |
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| 0.2658 | 9.0 | 567 | 0.3835 | 0.7536 | 0.6548 | 0.7007 | 0.9381 | |
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| 0.2658 | 10.0 | 630 | 0.3662 | 0.7455 | 0.6718 | 0.7067 | 0.9389 | |
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| 0.2658 | 11.0 | 693 | 0.3732 | 0.7394 | 0.6588 | 0.6967 | 0.9388 | |
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| 0.2658 | 12.0 | 756 | 0.3739 | 0.7505 | 0.6695 | 0.7077 | 0.9403 | |
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| 0.2658 | 13.0 | 819 | 0.3884 | 0.7513 | 0.6655 | 0.7058 | 0.9397 | |
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| 0.2658 | 14.0 | 882 | 0.3955 | 0.7609 | 0.6616 | 0.7078 | 0.9398 | |
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| 0.2658 | 15.0 | 945 | 0.3986 | 0.7689 | 0.6599 | 0.7102 | 0.9401 | |
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| 0.0369 | 16.0 | 1008 | 0.3975 | 0.7633 | 0.6723 | 0.7149 | 0.9408 | |
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| 0.0369 | 17.0 | 1071 | 0.3955 | 0.7437 | 0.6819 | 0.7115 | 0.9401 | |
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| 0.0369 | 18.0 | 1134 | 0.3968 | 0.7555 | 0.6808 | 0.7162 | 0.9408 | |
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| 0.0369 | 19.0 | 1197 | 0.3999 | 0.7527 | 0.6791 | 0.7140 | 0.9405 | |
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| 0.0369 | 20.0 | 1260 | 0.4000 | 0.7565 | 0.6791 | 0.7157 | 0.9405 | |
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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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