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--- |
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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: 1.0409 |
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- Precision: 0.6097 |
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- Recall: 0.6323 |
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- F1: 0.6208 |
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- Accuracy: 0.7607 |
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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 | 1.6300 | 0.3434 | 0.3838 | 0.3625 | 0.6077 | |
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| No log | 2.0 | 126 | 1.2289 | 0.4831 | 0.5207 | 0.5012 | 0.6893 | |
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| No log | 3.0 | 189 | 1.0878 | 0.5261 | 0.5762 | 0.5500 | 0.7197 | |
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| No log | 4.0 | 252 | 1.0253 | 0.5541 | 0.5914 | 0.5721 | 0.7328 | |
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| No log | 5.0 | 315 | 0.9738 | 0.5689 | 0.6040 | 0.5859 | 0.7416 | |
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| No log | 6.0 | 378 | 0.9498 | 0.5828 | 0.6094 | 0.5958 | 0.7472 | |
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| No log | 7.0 | 441 | 0.9532 | 0.5954 | 0.6126 | 0.6039 | 0.7509 | |
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| 1.1083 | 8.0 | 504 | 0.9515 | 0.5994 | 0.6166 | 0.6079 | 0.7530 | |
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| 1.1083 | 9.0 | 567 | 0.9572 | 0.6010 | 0.6212 | 0.6109 | 0.7547 | |
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| 1.1083 | 10.0 | 630 | 0.9690 | 0.5986 | 0.6162 | 0.6072 | 0.7539 | |
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| 1.1083 | 11.0 | 693 | 0.9798 | 0.5953 | 0.6232 | 0.6089 | 0.7532 | |
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| 1.1083 | 12.0 | 756 | 0.9813 | 0.5986 | 0.6185 | 0.6084 | 0.7546 | |
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| 1.1083 | 13.0 | 819 | 0.9984 | 0.5979 | 0.6182 | 0.6079 | 0.7539 | |
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| 1.1083 | 14.0 | 882 | 1.0111 | 0.6026 | 0.6226 | 0.6124 | 0.7557 | |
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| 1.1083 | 15.0 | 945 | 1.0140 | 0.6050 | 0.6262 | 0.6155 | 0.7572 | |
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| 0.4329 | 16.0 | 1008 | 1.0252 | 0.6112 | 0.6210 | 0.6160 | 0.7580 | |
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| 0.4329 | 17.0 | 1071 | 1.0312 | 0.6090 | 0.6288 | 0.6187 | 0.7602 | |
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| 0.4329 | 18.0 | 1134 | 1.0368 | 0.6059 | 0.6314 | 0.6184 | 0.7597 | |
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| 0.4329 | 19.0 | 1197 | 1.0395 | 0.6095 | 0.6299 | 0.6196 | 0.7599 | |
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| 0.4329 | 20.0 | 1260 | 1.0409 | 0.6097 | 0.6323 | 0.6208 | 0.7607 | |
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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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