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# JNLPBA_bioBERT_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: 0.1445
- Seqeval classification report:               precision    recall  f1-score   support

         DNA       0.72      0.80      0.76       507
         RNA       0.81      0.83      0.82      1593
   cell_line       0.76      0.78      0.77      5750
   cell_type       0.76      0.81      0.79       618
     protein       0.81      0.81      0.81      1452

   micro avg       0.77      0.80      0.78      9920
   macro avg       0.77      0.81      0.79      9920
weighted avg       0.77      0.80      0.78      9920


## 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 | Seqeval classification report                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.2687        | 1.0   | 582  | 0.1504          |               precision    recall  f1-score   support

         DNA       0.72      0.81      0.76       507
         RNA       0.78      0.82      0.80      1593
   cell_line       0.75      0.77      0.76      5750
   cell_type       0.76      0.81      0.78       618
     protein       0.80      0.81      0.80      1452

   micro avg       0.76      0.79      0.77      9920
   macro avg       0.76      0.80      0.78      9920
weighted avg       0.76      0.79      0.77      9920
 |
| 0.1412        | 2.0   | 1164 | 0.1461          |               precision    recall  f1-score   support

         DNA       0.72      0.81      0.76       507
         RNA       0.83      0.79      0.81      1593
   cell_line       0.75      0.77      0.76      5750
   cell_type       0.75      0.82      0.78       618
     protein       0.85      0.75      0.80      1452

   micro avg       0.78      0.78      0.78      9920
   macro avg       0.78      0.79      0.78      9920
weighted avg       0.78      0.78      0.78      9920
 |
| 0.1251        | 3.0   | 1746 | 0.1445          |               precision    recall  f1-score   support

         DNA       0.72      0.80      0.76       507
         RNA       0.81      0.83      0.82      1593
   cell_line       0.76      0.78      0.77      5750
   cell_type       0.76      0.81      0.79       618
     protein       0.81      0.81      0.81      1452

   micro avg       0.77      0.80      0.78      9920
   macro avg       0.77      0.81      0.79      9920
weighted avg       0.77      0.80      0.78      9920
 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

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