---
base_model: dmis-lab/biobert-v1.1
tags:
- generated_from_trainer
model-index:
- name: BC5CDR_bioBERT_NER
  results: []
---

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# BC5CDR_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: 0.0808
- Seqeval classification report:               precision    recall  f1-score   support

    Chemical       0.99      0.98      0.98    103336
     Disease       0.83      0.88      0.85      6944

   micro avg       0.97      0.97      0.97    110280
   macro avg       0.91      0.93      0.92    110280
weighted avg       0.98      0.97      0.97    110280


## 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                                                                                                                                                                                                                                                                                                          |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| No log        | 1.0   | 143  | 0.0903          |               precision    recall  f1-score   support

    Chemical       0.98      0.98      0.98    103336
     Disease       0.79      0.87      0.83      6944

   micro avg       0.97      0.97      0.97    110280
   macro avg       0.89      0.92      0.90    110280
weighted avg       0.97      0.97      0.97    110280
 |
| No log        | 2.0   | 286  | 0.0823          |               precision    recall  f1-score   support

    Chemical       0.99      0.98      0.98    103336
     Disease       0.79      0.87      0.83      6944

   micro avg       0.97      0.97      0.97    110280
   macro avg       0.89      0.92      0.91    110280
weighted avg       0.97      0.97      0.97    110280
 |
| No log        | 3.0   | 429  | 0.0808          |               precision    recall  f1-score   support

    Chemical       0.99      0.98      0.98    103336
     Disease       0.83      0.88      0.85      6944

   micro avg       0.97      0.97      0.97    110280
   macro avg       0.91      0.93      0.92    110280
weighted avg       0.98      0.97      0.97    110280
 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0