---
base_model: dmis-lab/biobert-v1.1
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: biobert-base-case-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2002
      type: conll2002
      config: es
      split: validation
      args: es
    metrics:
    - name: Precision
      type: precision
      value: 0.7494539100043687
    - name: Recall
      type: recall
      value: 0.7883731617647058
    - name: F1
      type: f1
      value: 0.7684210526315789
    - name: Accuracy
      type: accuracy
      value: 0.9629927984937011
---

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

This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the conll2002 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2531
- Precision: 0.7495
- Recall: 0.7884
- F1: 0.7684
- Accuracy: 0.9630

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1214        | 1.0   | 1041  | 0.1681          | 0.6611    | 0.6997 | 0.6798 | 0.9523   |
| 0.0814        | 2.0   | 2082  | 0.1652          | 0.6692    | 0.7270 | 0.6969 | 0.9540   |
| 0.0531        | 3.0   | 3123  | 0.1628          | 0.7291    | 0.7682 | 0.7481 | 0.9624   |
| 0.0357        | 4.0   | 4164  | 0.1799          | 0.7427    | 0.7721 | 0.7571 | 0.9620   |
| 0.0277        | 5.0   | 5205  | 0.1963          | 0.7530    | 0.7824 | 0.7674 | 0.9627   |
| 0.0168        | 6.0   | 6246  | 0.2115          | 0.7333    | 0.7771 | 0.7546 | 0.9615   |
| 0.0136        | 7.0   | 7287  | 0.2311          | 0.7376    | 0.7769 | 0.7567 | 0.9613   |
| 0.0106        | 8.0   | 8328  | 0.2450          | 0.7552    | 0.7861 | 0.7703 | 0.9626   |
| 0.0062        | 9.0   | 9369  | 0.2572          | 0.7589    | 0.7877 | 0.7730 | 0.9622   |
| 0.0061        | 10.0  | 10410 | 0.2531          | 0.7495    | 0.7884 | 0.7684 | 0.9630   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1