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---
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}
}
``` |