---
license: mit
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-wnut2017-en
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wnut_17
      type: wnut_17
      config: wnut_17
      split: validation
      args: wnut_17
    metrics:
    - name: Precision
      type: precision
      value: 0.7219662058371735
    - name: Recall
      type: recall
      value: 0.562200956937799
    - name: F1
      type: f1
      value: 0.6321452589105581
    - name: Accuracy
      type: accuracy
      value: 0.9589398080467807
---

<!-- 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-wnut2017-en

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on [wnut_17](https://huggingface.co/datasets/wnut_17) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2319
- Precision: 0.7220
- Recall: 0.5622
- F1: 0.6321
- Accuracy: 0.9589

## 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   | 107  | 0.2789          | 0.4679    | 0.3397 | 0.3936 | 0.9408   |
| No log        | 2.0   | 214  | 0.2092          | 0.6875    | 0.5    | 0.5789 | 0.9518   |
| No log        | 3.0   | 321  | 0.1968          | 0.6194    | 0.5431 | 0.5787 | 0.9567   |
| No log        | 4.0   | 428  | 0.2172          | 0.7212    | 0.5383 | 0.6164 | 0.9586   |
| 0.1523        | 5.0   | 535  | 0.2319          | 0.7220    | 0.5622 | 0.6321 | 0.9589   |
| 0.1523        | 6.0   | 642  | 0.2023          | 0.6180    | 0.5514 | 0.5828 | 0.9577   |
| 0.1523        | 7.0   | 749  | 0.2494          | 0.6895    | 0.5419 | 0.6068 | 0.9589   |
| 0.1523        | 8.0   | 856  | 0.2844          | 0.7018    | 0.5263 | 0.6015 | 0.9578   |
| 0.1523        | 9.0   | 963  | 0.2568          | 0.6808    | 0.5562 | 0.6122 | 0.9592   |
| 0.0294        | 10.0  | 1070 | 0.2453          | 0.6718    | 0.5754 | 0.6198 | 0.9596   |
| 0.0294        | 11.0  | 1177 | 0.2538          | 0.6933    | 0.5706 | 0.6260 | 0.9600   |
| 0.0294        | 12.0  | 1284 | 0.2638          | 0.6865    | 0.5658 | 0.6203 | 0.9593   |
| 0.0294        | 13.0  | 1391 | 0.2744          | 0.6764    | 0.5526 | 0.6083 | 0.9587   |
| 0.0294        | 14.0  | 1498 | 0.2714          | 0.6812    | 0.5622 | 0.6160 | 0.9590   |
| 0.0135        | 15.0  | 1605 | 0.2724          | 0.6830    | 0.5670 | 0.6196 | 0.9593   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
### Citation
If you used the datasets and models in this repository, please cite it.

```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}
}
```