---
license: mit
tags:
- generated_from_trainer
datasets: Amir13/ontonotes5-persian
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-ontonotesv5
  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-ontonotesv5


This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [ontonotes5-persian](https://huggingface.co/datasets/Amir13/ontonotes5-persian) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1693
- Precision: 0.8336
- Recall: 0.8360
- F1: 0.8348
- Accuracy: 0.9753

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1145        | 1.0   | 2310  | 0.1174          | 0.7717    | 0.7950 | 0.7832 | 0.9697   |
| 0.0793        | 2.0   | 4620  | 0.1084          | 0.8129    | 0.8108 | 0.8118 | 0.9729   |
| 0.0627        | 3.0   | 6930  | 0.1078          | 0.8227    | 0.8102 | 0.8164 | 0.9735   |
| 0.047         | 4.0   | 9240  | 0.1132          | 0.8105    | 0.8223 | 0.8164 | 0.9731   |
| 0.0347        | 5.0   | 11550 | 0.1190          | 0.8185    | 0.8315 | 0.8250 | 0.9742   |
| 0.0274        | 6.0   | 13860 | 0.1282          | 0.8088    | 0.8387 | 0.8235 | 0.9734   |
| 0.0202        | 7.0   | 16170 | 0.1329          | 0.8219    | 0.8354 | 0.8286 | 0.9745   |
| 0.0167        | 8.0   | 18480 | 0.1423          | 0.8147    | 0.8376 | 0.8260 | 0.9742   |
| 0.0134        | 9.0   | 20790 | 0.1520          | 0.8259    | 0.8308 | 0.8284 | 0.9745   |
| 0.0097        | 10.0  | 23100 | 0.1627          | 0.8226    | 0.8377 | 0.8300 | 0.9745   |
| 0.0084        | 11.0  | 25410 | 0.1693          | 0.8336    | 0.8360 | 0.8348 | 0.9753   |
| 0.0066        | 12.0  | 27720 | 0.1744          | 0.8317    | 0.8359 | 0.8338 | 0.9751   |
| 0.0053        | 13.0  | 30030 | 0.1764          | 0.8247    | 0.8409 | 0.8327 | 0.9750   |
| 0.004         | 14.0  | 32340 | 0.1797          | 0.8280    | 0.8378 | 0.8328 | 0.9751   |
| 0.004         | 15.0  | 34650 | 0.1809          | 0.8310    | 0.8382 | 0.8346 | 0.9754   |


### 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}
}
```