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# bert-base-parsbert-uncased-ontonotesv5

This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) on the [ontonotes5-persian](https://huggingface.co/datasets/Amir13/ontonotes5-persian) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2169
- Precision: 0.8145
- Recall: 0.8287
- F1: 0.8215
- Accuracy: 0.9741

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1029        | 1.0   | 2310  | 0.1151          | 0.8080    | 0.7559 | 0.7811 | 0.9691   |
| 0.059         | 2.0   | 4620  | 0.1098          | 0.7909    | 0.8068 | 0.7988 | 0.9719   |
| 0.0363        | 3.0   | 6930  | 0.1205          | 0.7981    | 0.8168 | 0.8074 | 0.9728   |
| 0.0202        | 4.0   | 9240  | 0.1406          | 0.8115    | 0.8046 | 0.8080 | 0.9726   |
| 0.0122        | 5.0   | 11550 | 0.1496          | 0.7847    | 0.8225 | 0.8031 | 0.9721   |
| 0.0105        | 6.0   | 13860 | 0.1633          | 0.7962    | 0.8188 | 0.8073 | 0.9724   |
| 0.0057        | 7.0   | 16170 | 0.1842          | 0.8071    | 0.8133 | 0.8102 | 0.9729   |
| 0.0041        | 8.0   | 18480 | 0.1913          | 0.8081    | 0.8093 | 0.8087 | 0.9727   |
| 0.003         | 9.0   | 20790 | 0.1935          | 0.8121    | 0.8130 | 0.8126 | 0.9732   |
| 0.002         | 10.0  | 23100 | 0.1992          | 0.8136    | 0.8214 | 0.8175 | 0.9734   |
| 0.002         | 11.0  | 25410 | 0.2037          | 0.8014    | 0.8280 | 0.8145 | 0.9735   |
| 0.0012        | 12.0  | 27720 | 0.2092          | 0.8133    | 0.8204 | 0.8168 | 0.9737   |
| 0.001         | 13.0  | 30030 | 0.2095          | 0.8125    | 0.8253 | 0.8188 | 0.9739   |
| 0.0006        | 14.0  | 32340 | 0.2143          | 0.8129    | 0.8272 | 0.8200 | 0.9740   |
| 0.0005        | 15.0  | 34650 | 0.2169          | 0.8145    | 0.8287 | 0.8215 | 0.9741   |


### 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-ontonotesv5", "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-ontonotesv5
======================================


This model is a fine-tuned version of HooshvareLab/bert-base-parsbert-uncased on the ontonotes5-persian dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2169
* Precision: 0.8145
* Recall: 0.8287
* F1: 0.8215
* Accuracy: 0.9741


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.