---
license: mit
tags:
- generated_from_trainer
datasets: Amir13/wnut2017-persian
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-wnut2017
  results: []
---

<!-- 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

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [wnut2017-persian](https://huggingface.co/datasets/Amir13/wnut2017-persian) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2943
- Precision: 0.5430
- Recall: 0.4181
- F1: 0.4724
- Accuracy: 0.9379

## 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   | 106  | 0.3715          | 0.0667    | 0.0012 | 0.0024 | 0.9119   |
| No log        | 2.0   | 212  | 0.3279          | 0.3482    | 0.1783 | 0.2359 | 0.9217   |
| No log        | 3.0   | 318  | 0.3008          | 0.5574    | 0.3627 | 0.4394 | 0.9344   |
| No log        | 4.0   | 424  | 0.2884          | 0.5226    | 0.3614 | 0.4274 | 0.9363   |
| 0.2149        | 5.0   | 530  | 0.2943          | 0.5430    | 0.4181 | 0.4724 | 0.9379   |
| 0.2149        | 6.0   | 636  | 0.3180          | 0.5338    | 0.3711 | 0.4378 | 0.9377   |
| 0.2149        | 7.0   | 742  | 0.3090          | 0.4993    | 0.4277 | 0.4607 | 0.9365   |
| 0.2149        | 8.0   | 848  | 0.3300          | 0.5300    | 0.4048 | 0.4590 | 0.9380   |
| 0.2149        | 9.0   | 954  | 0.3365          | 0.4938    | 0.3843 | 0.4322 | 0.9367   |
| 0.0623        | 10.0  | 1060 | 0.3363          | 0.5028    | 0.4313 | 0.4643 | 0.9363   |
| 0.0623        | 11.0  | 1166 | 0.3567          | 0.4992    | 0.3880 | 0.4366 | 0.9356   |
| 0.0623        | 12.0  | 1272 | 0.3681          | 0.5164    | 0.3988 | 0.4500 | 0.9375   |
| 0.0623        | 13.0  | 1378 | 0.3698          | 0.5086    | 0.3928 | 0.4432 | 0.9376   |
| 0.0623        | 14.0  | 1484 | 0.3690          | 0.5157    | 0.4157 | 0.4603 | 0.9380   |
| 0.0303        | 15.0  | 1590 | 0.3744          | 0.5045    | 0.4072 | 0.4507 | 0.9375   |


### 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}
}
```