| --- |
| license: mit |
| base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| model-index: |
| - name: test |
| 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. --> |
|
|
| # test |
|
|
| This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6886 |
| - Accuracy: 0.8143 |
| - F1: [0.92816572 0.56028369 0.1 0.2633452 ] |
|
|
| ## 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: 32 |
| - eval_batch_size: 64 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 64 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: cosine |
| - num_epochs: 10.0 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------------------------------------:| |
| | No log | 1.0 | 37 | 0.4891 | 0.8235 | [0.91702786 0.33333333 0. 0.10837438] | |
| | No log | 2.0 | 74 | 0.4762 | 0.8321 | [0.93139159 0.48466258 0. 0.22857143] | |
| | No log | 3.0 | 111 | 0.5084 | 0.8208 | [0.92995725 0.44887781 0. 0.19266055] | |
| | No log | 4.0 | 148 | 0.5519 | 0.8105 | [0.92421691 0.44444444 0.06557377 0.30769231] | |
| | No log | 5.0 | 185 | 0.5805 | 0.8294 | [0.93531353 0.52336449 0.09345794 0.27131783] | |
| | No log | 6.0 | 222 | 0.6778 | 0.7955 | [0.91344509 0.55305466 0.15463918 0.29166667] | |
| | No log | 7.0 | 259 | 0.6407 | 0.8213 | [0.93298292 0.51383399 0.10191083 0.2519084 ] | |
| | No log | 8.0 | 296 | 0.6639 | 0.8272 | [0.9326288 0.55052265 0.18181818 0.26271186] | |
| | No log | 9.0 | 333 | 0.6863 | 0.8192 | [0.93071286 0.55830389 0.11042945 0.2761194 ] | |
| | No log | 10.0 | 370 | 0.6886 | 0.8143 | [0.92816572 0.56028369 0.1 0.2633452 ] | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.37.2 |
| - Pytorch 2.2.0+cu121 |
| - Datasets 2.17.0 |
| - Tokenizers 0.15.2 |
| |
