---
base_model: FacebookAI/xlm-roberta-large-finetuned-conll03-english
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xml-roberta-large-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: 0.880600409370025
    - name: Recall
      type: recall
      value: 0.8897058823529411
    - name: F1
      type: f1
      value: 0.8851297291118985
    - name: Accuracy
      type: accuracy
      value: 0.9806463992982264
---

<!-- 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. -->

# xml-roberta-large-finetuned-ner

Este es modelo resultado de un finetuning de 
[FacebookAI/xlm-roberta-large-finetuned-conll03-english](https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll03-english) sobre el conll2002 dataset.
Los siguientes son los resultados sobre el conjunto de evaluación:
- Loss: 0.1364
- Precision: 0.8806
- Recall: 0.8897
- F1: 0.8851
- Accuracy: 0.9806

## Model description

Este es el modelo más grande de roberta [FacebookAI/xlm-roberta-large-finetuned-conll03-english](https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll03-english)-
Este modelo fue ajustado usando el framework Kaggle [https://www.kaggle.com/settings]. Para realizar el preentrenamiento del modelo se tuvo que crear un directorio temporal en Kaggle
con el fin de almacenar de manera temoporal el modelo que pesa alrededor de 35 Gz.

## 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: 4
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0743        | 1.0   | 2081  | 0.1131          | 0.8385    | 0.8587 | 0.8485 | 0.9771   |
| 0.049         | 2.0   | 4162  | 0.1429          | 0.8492    | 0.8564 | 0.8528 | 0.9756   |
| 0.031         | 3.0   | 6243  | 0.1298          | 0.8758    | 0.8817 | 0.8787 | 0.9800   |
| 0.0185        | 4.0   | 8324  | 0.1279          | 0.8827    | 0.8890 | 0.8859 | 0.9808   |
| 0.0125        | 5.0   | 10405 | 0.1364          | 0.8806    | 0.8897 | 0.8851 | 0.9806   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1