---
base_model: dmis-lab/biobert-v1.1
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BioBERT_BioNLP13CG_NER_new
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# BioBERT_BioNLP13CG_NER_new

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: 0.1721
- Precision: 0.8444
- Recall: 0.8396
- F1: 0.8420
- Accuracy: 0.9571

## 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
- 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        | 1.0   | 191  | 0.2112          | 0.8333    | 0.8134 | 0.8232 | 0.9507   |
| No log        | 2.0   | 382  | 0.1744          | 0.8304    | 0.8400 | 0.8352 | 0.9557   |
| 0.3204        | 3.0   | 573  | 0.1721          | 0.8444    | 0.8396 | 0.8420 | 0.9571   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0