| | --- |
| | license: mit |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: New_BioRED_model_1 |
| | 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. --> |
| |
|
| | # New_BioRED_model_1 |
| | |
| | This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.4105 |
| | - Precision: 0.4596 |
| | - Recall: 0.2902 |
| | - F1: 0.3558 |
| | - Accuracy: 0.8605 |
| | |
| | ## 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: 1e-05 |
| | - train_batch_size: 32 |
| | - eval_batch_size: 32 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 15 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | No log | 1.0 | 13 | 0.7199 | 0.0 | 0.0 | 0.0 | 0.8273 | |
| | | No log | 2.0 | 26 | 0.6140 | 0.1663 | 0.0112 | 0.0210 | 0.8316 | |
| | | No log | 3.0 | 39 | 0.5403 | 0.3494 | 0.1336 | 0.1933 | 0.8462 | |
| | | No log | 4.0 | 52 | 0.4823 | 0.3732 | 0.1895 | 0.2514 | 0.8501 | |
| | | No log | 5.0 | 65 | 0.4644 | 0.3951 | 0.2304 | 0.2911 | 0.8534 | |
| | | No log | 6.0 | 78 | 0.4450 | 0.4086 | 0.2515 | 0.3114 | 0.8553 | |
| | | No log | 7.0 | 91 | 0.4324 | 0.4293 | 0.2667 | 0.3290 | 0.8570 | |
| | | No log | 8.0 | 104 | 0.4242 | 0.4413 | 0.2684 | 0.3338 | 0.8583 | |
| | | No log | 9.0 | 117 | 0.4209 | 0.4452 | 0.2773 | 0.3417 | 0.8587 | |
| | | No log | 10.0 | 130 | 0.4170 | 0.4499 | 0.2854 | 0.3493 | 0.8593 | |
| | | No log | 11.0 | 143 | 0.4131 | 0.4568 | 0.2891 | 0.3541 | 0.8600 | |
| | | No log | 12.0 | 156 | 0.4140 | 0.4478 | 0.2962 | 0.3566 | 0.8588 | |
| | | No log | 13.0 | 169 | 0.4120 | 0.4660 | 0.2889 | 0.3567 | 0.8608 | |
| | | No log | 14.0 | 182 | 0.4116 | 0.4560 | 0.2911 | 0.3554 | 0.8600 | |
| | | No log | 15.0 | 195 | 0.4105 | 0.4596 | 0.2902 | 0.3558 | 0.8605 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.26.1 |
| | - Pytorch 1.12.1 |
| | - Datasets 2.6.1 |
| | - Tokenizers 0.11.0 |
| | |
