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

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [ncbi-persian](https://huggingface.co/datasets/Amir13/ncbi-persian) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0915
- Precision: 0.8273
- Recall: 0.8763
- F1: 0.8511
- Accuracy: 0.9866

## 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   | 169  | 0.0682          | 0.7049    | 0.7763 | 0.7389 | 0.9784   |
| No log        | 2.0   | 338  | 0.0575          | 0.7558    | 0.8592 | 0.8042 | 0.9832   |
| 0.0889        | 3.0   | 507  | 0.0558          | 0.8092    | 0.8592 | 0.8334 | 0.9859   |
| 0.0889        | 4.0   | 676  | 0.0595          | 0.8316    | 0.8579 | 0.8446 | 0.9858   |
| 0.0889        | 5.0   | 845  | 0.0665          | 0.7998    | 0.8566 | 0.8272 | 0.9850   |
| 0.0191        | 6.0   | 1014 | 0.0796          | 0.8229    | 0.85   | 0.8362 | 0.9862   |
| 0.0191        | 7.0   | 1183 | 0.0783          | 0.8193    | 0.8474 | 0.8331 | 0.9860   |
| 0.0191        | 8.0   | 1352 | 0.0792          | 0.8257    | 0.8539 | 0.8396 | 0.9864   |
| 0.0079        | 9.0   | 1521 | 0.0847          | 0.8154    | 0.8658 | 0.8398 | 0.9851   |
| 0.0079        | 10.0  | 1690 | 0.0855          | 0.8160    | 0.875  | 0.8444 | 0.9857   |
| 0.0079        | 11.0  | 1859 | 0.0868          | 0.8081    | 0.8645 | 0.8353 | 0.9864   |
| 0.0037        | 12.0  | 2028 | 0.0912          | 0.8036    | 0.8776 | 0.8390 | 0.9853   |
| 0.0037        | 13.0  | 2197 | 0.0907          | 0.8323    | 0.8684 | 0.8500 | 0.9868   |
| 0.0037        | 14.0  | 2366 | 0.0899          | 0.8192    | 0.8763 | 0.8468 | 0.9865   |
| 0.0023        | 15.0  | 2535 | 0.0915          | 0.8273    | 0.8763 | 0.8511 | 0.9866   |


### 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}
}
```