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

# bert-base-parsbert-uncased-ncbi_disease

This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) on the [ncbi-persian](https://huggingface.co/datasets/Amir13/ncbi-persian) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1018
- Precision: 0.8192
- Recall: 0.8645
- F1: 0.8412
- Accuracy: 0.9862

## 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  | 0.0648          | 0.7154    | 0.8237 | 0.7657 | 0.9813   |
| No log        | 2.0   | 338  | 0.0573          | 0.7870    | 0.8263 | 0.8062 | 0.9853   |
| 0.0596        | 3.0   | 507  | 0.0639          | 0.7893    | 0.8776 | 0.8312 | 0.9858   |
| 0.0596        | 4.0   | 676  | 0.0678          | 0.8150    | 0.8461 | 0.8302 | 0.9860   |
| 0.0596        | 5.0   | 845  | 0.0737          | 0.8070    | 0.8474 | 0.8267 | 0.9862   |
| 0.0065        | 6.0   | 1014 | 0.0834          | 0.8052    | 0.8592 | 0.8313 | 0.9856   |
| 0.0065        | 7.0   | 1183 | 0.0918          | 0.8099    | 0.8355 | 0.8225 | 0.9859   |
| 0.0065        | 8.0   | 1352 | 0.0882          | 0.8061    | 0.8697 | 0.8367 | 0.9857   |
| 0.0021        | 9.0   | 1521 | 0.0903          | 0.8045    | 0.85   | 0.8266 | 0.9860   |
| 0.0021        | 10.0  | 1690 | 0.0965          | 0.8303    | 0.85   | 0.8401 | 0.9866   |
| 0.0021        | 11.0  | 1859 | 0.0954          | 0.8182    | 0.8645 | 0.8407 | 0.9860   |
| 0.0008        | 12.0  | 2028 | 0.0998          | 0.8206    | 0.8605 | 0.8401 | 0.9862   |
| 0.0008        | 13.0  | 2197 | 0.0995          | 0.82      | 0.8632 | 0.8410 | 0.9862   |
| 0.0008        | 14.0  | 2366 | 0.1015          | 0.8214    | 0.8592 | 0.8399 | 0.9861   |
| 0.0004        | 15.0  | 2535 | 0.1018          | 0.8192    | 0.8645 | 0.8412 | 0.9862   |


### 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}
}
```