---
tags:
- generated_from_trainer
datasets:
- jnlpba
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: biobert-base-cased-v1.2-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: jnlpba
      type: jnlpba
      args: jnlpba
    metrics:
    - name: Precision
      type: precision
      value: 0.7193353093271111
    - name: Recall
      type: recall
      value: 0.8325912408759124
    - name: F1
      type: f1
      value: 0.7718307000033834
    - name: Accuracy
      type: accuracy
      value: 0.9057438991228902
---

<!-- 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-base-cased-v1.2-finetuned-ner

This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the jnlpba dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3674
- Precision: 0.7193
- Recall: 0.8326
- F1: 0.7718
- Accuracy: 0.9057

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2584        | 1.0   | 1160 | 0.2930          | 0.7052    | 0.8246 | 0.7603 | 0.9019   |
| 0.1966        | 2.0   | 2320 | 0.3023          | 0.7175    | 0.8247 | 0.7674 | 0.9056   |
| 0.1577        | 3.0   | 3480 | 0.3171          | 0.7165    | 0.8228 | 0.7659 | 0.9047   |
| 0.131         | 4.0   | 4640 | 0.3413          | 0.7201    | 0.8292 | 0.7708 | 0.9054   |
| 0.1073        | 5.0   | 5800 | 0.3674          | 0.7193    | 0.8326 | 0.7718 | 0.9057   |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.1+cu102
- Datasets 1.13.2
- Tokenizers 0.10.3