---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Plant-gob-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: 0.8627583108715184
    - name: Recall
      type: recall
      value: 0.8825827205882353
    - name: F1
      type: f1
      value: 0.872557928214448
    - name: Accuracy
      type: accuracy
      value: 0.9784878927600843
---

<!-- 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. -->

# Plant-gob-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: 0.1071
- Precision: 0.8628
- Recall: 0.8826
- F1: 0.8726
- Accuracy: 0.9785

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0681        | 1.0   | 521  | 0.0818          | 0.8645    | 0.8842 | 0.8742 | 0.9789   |
| 0.0313        | 2.0   | 1042 | 0.0841          | 0.8516    | 0.8768 | 0.8640 | 0.9779   |
| 0.0179        | 3.0   | 1563 | 0.0979          | 0.8584    | 0.8773 | 0.8677 | 0.9781   |
| 0.0091        | 4.0   | 2084 | 0.0996          | 0.8600    | 0.8819 | 0.8708 | 0.9784   |
| 0.0068        | 5.0   | 2605 | 0.1071          | 0.8628    | 0.8826 | 0.8726 | 0.9785   |


### Framework versions

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