---
license: apache-2.0
base_model: BSC-LT/roberta-base-bne-capitel-ner
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-bne-capitel-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: 0.8601446000903751
    - name: Recall
      type: recall
      value: 0.8747702205882353
    - name: F1
      type: f1
      value: 0.8673957621326043
    - name: Accuracy
      type: accuracy
      value: 0.9779993282237626
---

<!-- 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. -->

# roberta-base-bne-capitel-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: 0.1229
- Precision: 0.8601
- Recall: 0.8748
- F1: 0.8674
- Accuracy: 0.9780

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0172        | 1.0   | 1041 | 0.1157          | 0.8468    | 0.8640 | 0.8553 | 0.9770   |
| 0.0109        | 2.0   | 2082 | 0.1177          | 0.8705    | 0.8853 | 0.8779 | 0.9786   |
| 0.0066        | 3.0   | 3123 | 0.1229          | 0.8601    | 0.8748 | 0.8674 | 0.9780   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.14.1