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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: 1.0318 |
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- Precision: 0.6083 |
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- Recall: 0.6344 |
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- F1: 0.6210 |
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- Accuracy: 0.7635 |
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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 | 1.6731 | 0.3451 | 0.3549 | 0.3499 | 0.6003 | |
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| No log | 2.0 | 126 | 1.2481 | 0.4832 | 0.5248 | 0.5032 | 0.6912 | |
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| No log | 3.0 | 189 | 1.0959 | 0.5280 | 0.5703 | 0.5483 | 0.7198 | |
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| No log | 4.0 | 252 | 1.0258 | 0.5577 | 0.5878 | 0.5723 | 0.7330 | |
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| No log | 5.0 | 315 | 0.9761 | 0.5788 | 0.6038 | 0.5910 | 0.7433 | |
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| No log | 6.0 | 378 | 0.9461 | 0.5909 | 0.6068 | 0.5988 | 0.7478 | |
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| No log | 7.0 | 441 | 0.9456 | 0.5918 | 0.6143 | 0.6029 | 0.7516 | |
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| 1.1189 | 8.0 | 504 | 0.9396 | 0.5991 | 0.6164 | 0.6076 | 0.7562 | |
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| 1.1189 | 9.0 | 567 | 0.9594 | 0.6020 | 0.6252 | 0.6134 | 0.7569 | |
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| 1.1189 | 10.0 | 630 | 0.9742 | 0.6005 | 0.6203 | 0.6102 | 0.7555 | |
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| 1.1189 | 11.0 | 693 | 0.9700 | 0.6063 | 0.6256 | 0.6158 | 0.7597 | |
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| 1.1189 | 12.0 | 756 | 0.9772 | 0.5999 | 0.6246 | 0.6120 | 0.7582 | |
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| 1.1189 | 13.0 | 819 | 0.9890 | 0.6023 | 0.6254 | 0.6137 | 0.7593 | |
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| 1.1189 | 14.0 | 882 | 1.0011 | 0.6077 | 0.6332 | 0.6202 | 0.7631 | |
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| 1.1189 | 15.0 | 945 | 1.0096 | 0.6057 | 0.6305 | 0.6178 | 0.7613 | |
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| 0.4346 | 16.0 | 1008 | 1.0167 | 0.6112 | 0.6281 | 0.6195 | 0.7625 | |
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| 0.4346 | 17.0 | 1071 | 1.0227 | 0.6126 | 0.6357 | 0.6240 | 0.7651 | |
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| 0.4346 | 18.0 | 1134 | 1.0290 | 0.6072 | 0.6357 | 0.6212 | 0.7635 | |
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| 0.4346 | 19.0 | 1197 | 1.0288 | 0.6094 | 0.6363 | 0.6226 | 0.7643 | |
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| 0.4346 | 20.0 | 1260 | 1.0318 | 0.6083 | 0.6344 | 0.6210 | 0.7635 | |
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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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