---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-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: 0.8734693877551021
    - name: Recall
      type: recall
      value: 0.8851102941176471
    - name: F1
      type: f1
      value: 0.8792513124857338
    - name: Accuracy
      type: accuracy
      value: 0.979541360041528
---

<!-- 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. -->

# bert-finetuned-ner

This model is a fine-tuned version of [BSC-LT/roberta-base-bne-capitel-ner](https://huggingface.co/BSC-LT/roberta-base-bne-capitel-ner) on the conll2002 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0911
- Precision: 0.8735
- Recall: 0.8851
- F1: 0.8793
- Accuracy: 0.9795

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0981        | 1.0   | 521  | 0.0849          | 0.8612    | 0.8814 | 0.8712 | 0.9789   |
| 0.0327        | 2.0   | 1042 | 0.0833          | 0.8634    | 0.8814 | 0.8723 | 0.9796   |
| 0.0193        | 3.0   | 1563 | 0.0911          | 0.8735    | 0.8851 | 0.8793 | 0.9795   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3