---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-parsbert-uncased-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. -->

# bert-base-parsbert-uncased-wnut2017

This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) on the [wnut2017-persian](https://huggingface.co/datasets/Amir13/wnut2017-persian) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4473
- Precision: 0.5374
- Recall: 0.4072
- F1: 0.4633
- Accuracy: 0.9375

## 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  | 0.3045          | 0.5994    | 0.2506 | 0.3534 | 0.9310   |
| No log        | 2.0   | 212  | 0.3051          | 0.5980    | 0.2940 | 0.3942 | 0.9352   |
| No log        | 3.0   | 318  | 0.2949          | 0.5284    | 0.3807 | 0.4426 | 0.9369   |
| No log        | 4.0   | 424  | 0.3382          | 0.5190    | 0.3940 | 0.4479 | 0.9368   |
| 0.1264        | 5.0   | 530  | 0.3700          | 0.5056    | 0.3783 | 0.4328 | 0.9352   |
| 0.1264        | 6.0   | 636  | 0.3975          | 0.4938    | 0.3867 | 0.4338 | 0.9350   |
| 0.1264        | 7.0   | 742  | 0.4587          | 0.5450    | 0.3795 | 0.4474 | 0.9369   |
| 0.1264        | 8.0   | 848  | 0.4473          | 0.5374    | 0.4072 | 0.4633 | 0.9375   |
| 0.1264        | 9.0   | 954  | 0.4940          | 0.5313    | 0.3578 | 0.4276 | 0.9362   |
| 0.0126        | 10.0  | 1060 | 0.5195          | 0.5631    | 0.3494 | 0.4312 | 0.9365   |
| 0.0126        | 11.0  | 1166 | 0.4825          | 0.5449    | 0.3952 | 0.4581 | 0.9371   |
| 0.0126        | 12.0  | 1272 | 0.4862          | 0.5288    | 0.3976 | 0.4539 | 0.9369   |
| 0.0126        | 13.0  | 1378 | 0.5017          | 0.5459    | 0.3867 | 0.4528 | 0.9373   |
| 0.0126        | 14.0  | 1484 | 0.4963          | 0.5403    | 0.3880 | 0.4516 | 0.9371   |
| 0.0032        | 15.0  | 1590 | 0.5035          | 0.5481    | 0.3843 | 0.4518 | 0.9371   |


### 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}
}
```