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# 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

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "dmis-lab/biobert-v1.1", "model-index": [{"name": "BioBERT_BioNLP13CG_NER", "results": []}]}
transformers tensorboard safetensors bert token-classification generated_from_trainer base_model:dmis-lab/biobert-v1.1 autotrain_compatible endpoints_compatible region:us