| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| metrics: |
| - f1 |
| model-index: |
| - name: Bio_ClinicalBERT_fold_10_binary_v1 |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # Bio_ClinicalBERT_fold_10_binary_v1 |
| |
| This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.5504 |
| - F1: 0.8243 |
| |
| ## Model description |
| |
| More information needed |
| |
| ## Intended uses & limitations |
| |
| More information needed |
| |
| ## Training and evaluation data |
| |
| More information needed |
| |
| ## Training procedure |
| |
| ### Training hyperparameters |
| |
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 25 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:------:| |
| | No log | 1.0 | 288 | 0.3803 | 0.8103 | |
| | 0.4005 | 2.0 | 576 | 0.4769 | 0.8070 | |
| | 0.4005 | 3.0 | 864 | 0.5258 | 0.7955 | |
| | 0.1889 | 4.0 | 1152 | 0.7423 | 0.8153 | |
| | 0.1889 | 5.0 | 1440 | 1.1246 | 0.8012 | |
| | 0.0703 | 6.0 | 1728 | 1.1325 | 0.8039 | |
| | 0.0246 | 7.0 | 2016 | 1.2192 | 0.8196 | |
| | 0.0246 | 8.0 | 2304 | 1.3645 | 0.8050 | |
| | 0.0192 | 9.0 | 2592 | 1.4029 | 0.8087 | |
| | 0.0192 | 10.0 | 2880 | 1.3714 | 0.8117 | |
| | 0.0107 | 11.0 | 3168 | 1.4673 | 0.8092 | |
| | 0.0107 | 12.0 | 3456 | 1.3941 | 0.8199 | |
| | 0.0084 | 13.0 | 3744 | 1.4350 | 0.8126 | |
| | 0.0083 | 14.0 | 4032 | 1.4428 | 0.8162 | |
| | 0.0083 | 15.0 | 4320 | 1.2892 | 0.8263 | |
| | 0.0119 | 16.0 | 4608 | 1.4238 | 0.8222 | |
| | 0.0119 | 17.0 | 4896 | 1.4961 | 0.8174 | |
| | 0.0046 | 18.0 | 5184 | 1.5010 | 0.8107 | |
| | 0.0046 | 19.0 | 5472 | 1.4876 | 0.8215 | |
| | 0.0036 | 20.0 | 5760 | 1.5080 | 0.8180 | |
| | 0.0031 | 21.0 | 6048 | 1.5317 | 0.8261 | |
| | 0.0031 | 22.0 | 6336 | 1.5103 | 0.8215 | |
| | 0.0005 | 23.0 | 6624 | 1.5255 | 0.8197 | |
| | 0.0005 | 24.0 | 6912 | 1.5578 | 0.8257 | |
| | 0.0001 | 25.0 | 7200 | 1.5504 | 0.8243 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.21.0 |
| - Pytorch 1.12.0+cu113 |
| - Datasets 2.4.0 |
| - Tokenizers 0.12.1 |
| |
