---
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: 0.6530483972344437
    - name: Recall
      type: recall
      value: 0.7162224264705882
    - name: F1
      type: f1
      value: 0.6831780821917808
    - name: Accuracy
      type: accuracy
      value: 0.9547333889783954
---

<!-- 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: 0.1655
- Precision: 0.6530
- Recall: 0.7162
- F1: 0.6832
- Accuracy: 0.9547

## 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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1257        | 1.0   | 1041 | 0.1724          | 0.6232    | 0.6788 | 0.6498 | 0.9510   |
| 0.0846        | 2.0   | 2082 | 0.1655          | 0.6530    | 0.7162 | 0.6832 | 0.9547   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1