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---
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: 0.8712310133756518
    - name: Recall
      type: recall
      value: 0.8830422794117647
    - name: F1
      type: f1
      value: 0.8770968846285518
    - name: Accuracy
      type: accuracy
      value: 0.978961189654646
---

<!-- 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: 0.1255
- Precision: 0.8712
- Recall: 0.8830
- F1: 0.8771
- Accuracy: 0.9790

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0135        | 1.0   | 1041 | 0.1233          | 0.8615    | 0.8803 | 0.8708 | 0.9783   |
| 0.0111        | 2.0   | 2082 | 0.1099          | 0.8709    | 0.8853 | 0.8781 | 0.9799   |
| 0.0061        | 3.0   | 3123 | 0.1203          | 0.8569    | 0.8739 | 0.8653 | 0.9781   |
| 0.0035        | 4.0   | 4164 | 0.1255          | 0.8712    | 0.8830 | 0.8771 | 0.9790   |


### Framework versions

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