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# bert-base-parsbert-uncased-conll2003

This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) on the [conll2003-persian](https://huggingface.co/datasets/Amir13/conll2003-persian
) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1631
- Precision: 0.8776
- Recall: 0.8898
- F1: 0.8836
- Accuracy: 0.9765

## 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   | 430  | 0.1063          | 0.8404    | 0.8476 | 0.8440 | 0.9696   |
| 0.1854        | 2.0   | 860  | 0.0982          | 0.8694    | 0.8696 | 0.8695 | 0.9743   |
| 0.0589        | 3.0   | 1290 | 0.1051          | 0.8649    | 0.8775 | 0.8712 | 0.9741   |
| 0.0285        | 4.0   | 1720 | 0.1233          | 0.8700    | 0.8787 | 0.8743 | 0.9745   |
| 0.0136        | 5.0   | 2150 | 0.1360          | 0.8700    | 0.8738 | 0.8719 | 0.9745   |
| 0.0077        | 6.0   | 2580 | 0.1390          | 0.8785    | 0.8812 | 0.8799 | 0.9754   |
| 0.0046        | 7.0   | 3010 | 0.1438          | 0.8803    | 0.8827 | 0.8815 | 0.9760   |
| 0.0046        | 8.0   | 3440 | 0.1510          | 0.8763    | 0.8794 | 0.8779 | 0.9756   |
| 0.0027        | 9.0   | 3870 | 0.1606          | 0.8798    | 0.8851 | 0.8824 | 0.9764   |
| 0.0021        | 10.0  | 4300 | 0.1631          | 0.8776    | 0.8898 | 0.8836 | 0.9765   |
| 0.0015        | 11.0  | 4730 | 0.1649          | 0.8782    | 0.8827 | 0.8804 | 0.9760   |
| 0.001         | 12.0  | 5160 | 0.1646          | 0.8787    | 0.8829 | 0.8808 | 0.9761   |
| 0.0008        | 13.0  | 5590 | 0.1686          | 0.8811    | 0.8846 | 0.8829 | 0.9765   |
| 0.0006        | 14.0  | 6020 | 0.1714          | 0.8820    | 0.8831 | 0.8825 | 0.9765   |
| 0.0006        | 15.0  | 6450 | 0.1706          | 0.8814    | 0.8838 | 0.8826 | 0.9764   |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- 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}
}
```
{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-parsbert-uncased-conll2003", "results": []}]}
transformers pytorch bert token-classification generated_from_trainer arxiv:2302.09611 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #arxiv-2302.09611 #autotrain_compatible #endpoints_compatible #region-us 

bert-base-parsbert-uncased-conll2003
====================================


This model is a fine-tuned version of HooshvareLab/bert-base-parsbert-uncased on the conll2003-persian dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1631
* Precision: 0.8776
* Recall: 0.8898
* F1: 0.8836
* Accuracy: 0.9765


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



### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2


If you used the datasets and models in this repository, please cite it.



### 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 ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2


If you used the datasets and models in this repository, please cite it.
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #arxiv-2302.09611 #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2


If you used the datasets and models in this repository, please cite it.
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #arxiv-2302.09611 #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2


If you used the datasets and models in this repository, please cite it.