| --- |
| license: mit |
| base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - 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-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: 0.4018 |
| - Accuracy: 0.8207 |
| - Precision: 0.8202 |
| - Recall: 0.8207 |
| - F1: 0.8202 |
|
|
| ## 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: 3e-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: 4 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | 0.4749 | 1.0 | 417 | 0.4018 | 0.8207 | 0.8202 | 0.8207 | 0.8202 | |
| | 0.0976 | 2.0 | 834 | 0.4443 | 0.8189 | 0.8234 | 0.8189 | 0.8197 | |
| | 0.0061 | 3.0 | 1251 | 0.7378 | 0.8213 | 0.8233 | 0.8213 | 0.8219 | |
| | 0.3159 | 4.0 | 1668 | 0.9154 | 0.8094 | 0.8092 | 0.8094 | 0.8092 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.35.2 |
| - Pytorch 2.1.0+cu121 |
| - Datasets 2.15.0 |
| - Tokenizers 0.15.0 |
|
|
