---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-large-finetuned-conll2003
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: validation
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.9620781824256599
    - name: Recall
      type: recall
      value: 0.9692022887916526
    - name: F1
      type: f1
      value: 0.9656270959087861
    - name: Accuracy
      type: accuracy
      value: 0.9936723647833028
---

<!-- 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-conll2003

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0412
- Precision: 0.9621
- Recall: 0.9692
- F1: 0.9656
- Accuracy: 0.9937

## 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: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1591        | 1.0   | 896  | 0.0440          | 0.9388    | 0.9451 | 0.9420 | 0.9896   |
| 0.0335        | 2.0   | 1792 | 0.0361          | 0.9512    | 0.9586 | 0.9549 | 0.9924   |
| 0.0195        | 3.0   | 2688 | 0.0378          | 0.9570    | 0.9636 | 0.9603 | 0.9931   |
| 0.0104        | 4.0   | 3584 | 0.0396          | 0.9587    | 0.9613 | 0.9600 | 0.9928   |
| 0.0064        | 5.0   | 4480 | 0.0400          | 0.9617    | 0.9675 | 0.9646 | 0.9937   |
| 0.0032        | 6.0   | 5376 | 0.0412          | 0.9621    | 0.9692 | 0.9656 | 0.9937   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0