---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-conll2003-en
  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: 0.9478680879413725
    - name: Recall
      type: recall
      value: 0.9588879528222409
    - name: F1
      type: f1
      value: 0.9533461763966831
    - name: Accuracy
      type: accuracy
      value: 0.9917972098823162
---

<!-- 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-base-conll2003-en

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [conll2003](https://huggingface.co/datasets/conll2003) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0534
- Precision: 0.9479
- Recall: 0.9589
- F1: 0.9533
- Accuracy: 0.9918

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 439  | 0.0535          | 0.9131    | 0.9238 | 0.9184 | 0.9865   |
| 0.1663        | 2.0   | 878  | 0.0461          | 0.9305    | 0.9390 | 0.9348 | 0.9887   |
| 0.0404        | 3.0   | 1317 | 0.0366          | 0.9431    | 0.9501 | 0.9466 | 0.9910   |
| 0.0252        | 4.0   | 1756 | 0.0381          | 0.9395    | 0.9516 | 0.9455 | 0.9908   |
| 0.0172        | 5.0   | 2195 | 0.0398          | 0.9409    | 0.9523 | 0.9466 | 0.9911   |
| 0.0119        | 6.0   | 2634 | 0.0429          | 0.9389    | 0.9560 | 0.9474 | 0.9910   |
| 0.0091        | 7.0   | 3073 | 0.0463          | 0.9451    | 0.9548 | 0.9500 | 0.9913   |
| 0.0063        | 8.0   | 3512 | 0.0446          | 0.9478    | 0.9575 | 0.9526 | 0.9919   |
| 0.0063        | 9.0   | 3951 | 0.0513          | 0.9424    | 0.9569 | 0.9496 | 0.9911   |
| 0.0049        | 10.0  | 4390 | 0.0494          | 0.9470    | 0.9545 | 0.9507 | 0.9915   |
| 0.0036        | 11.0  | 4829 | 0.0506          | 0.9477    | 0.9553 | 0.9515 | 0.9917   |
| 0.0029        | 12.0  | 5268 | 0.0518          | 0.9472    | 0.9586 | 0.9529 | 0.9919   |
| 0.0026        | 13.0  | 5707 | 0.0530          | 0.9451    | 0.9567 | 0.9508 | 0.9916   |
| 0.0021        | 14.0  | 6146 | 0.0526          | 0.9468    | 0.9567 | 0.9517 | 0.9917   |
| 0.0016        | 15.0  | 6585 | 0.0534          | 0.9479    | 0.9589 | 0.9533 | 0.9918   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
### Citation
```bibtex
@misc{https://doi.org/10.48550/arxiv.2302.09611,
  doi = {10.48550/ARXIV.2302.09611},
  url = {https://arxiv.org/abs/2302.09611},
  author = {Sartipi, Amir and Fatemi, Afsaneh},
  keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English},
  publisher = {arXiv},
  year = {2023},
  copyright = {arXiv.org perpetual, non-exclusive license}
}
```