| --- |
| base_model: dmis-lab/biobert-v1.1 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: BioBERT_BioNLP13CG_NER |
| 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. --> |
|
|
| # BioBERT_BioNLP13CG_NER |
|
|
| This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1954 |
| - Precision: 0.8710 |
| - Recall: 0.8602 |
| - F1: 0.8656 |
| - Accuracy: 0.9540 |
|
|
| ## 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 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 32 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 3 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 0.99 | 95 | 0.3032 | 0.8114 | 0.7836 | 0.7973 | 0.9291 | |
| | No log | 2.0 | 191 | 0.2073 | 0.8548 | 0.8532 | 0.8540 | 0.9498 | |
| | No log | 2.98 | 285 | 0.1954 | 0.8710 | 0.8602 | 0.8656 | 0.9540 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.35.2 |
| - Pytorch 2.1.0+cu121 |
| - Datasets 2.16.0 |
| - Tokenizers 0.15.0 |
| |
