---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-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: 0.86443345323741
    - name: Recall
      type: recall
      value: 0.8835018382352942
    - name: F1
      type: f1
      value: 0.8738636363636364
    - name: Accuracy
      type: accuracy
      value: 0.9787686065955755
---

<!-- 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. -->

# xlm-roberta-large-finetuned-ner

This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the conll2002 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0973
- Precision: 0.8644
- Recall: 0.8835
- F1: 0.8739
- Accuracy: 0.9788

## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1382        | 1.0   | 521  | 0.0906          | 0.8502    | 0.8830 | 0.8663 | 0.9782   |
| 0.048         | 2.0   | 1042 | 0.0861          | 0.8472    | 0.8729 | 0.8599 | 0.9780   |
| 0.0294        | 3.0   | 1563 | 0.0973          | 0.8644    | 0.8835 | 0.8739 | 0.9788   |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3