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# pft-clf-finetuned

This model is a fine-tuned version of [HooshvareLab/bert-fa-zwnj-base](https://huggingface.co/HooshvareLab/bert-fa-zwnj-base) on an "FarsNews1398" dataset. This dataset contains a collection of news that has been gathered from the farsnews website which is a news agency in Iran. You can download the dataset from [here](https://www.kaggle.com/amirhossein76/farsnews1398). I used category, abstract, and paragraphs of news for doing text classification. "abstract" and "paragraphs" for each news concatenated together and "category" used as a target for classification.

The notebook used for fine-tuning can be found [here](https://colab.research.google.com/drive/1jC2dfKRASxCY-b6bJSPkhEJfQkOA30O0?usp=sharing). I've reported loss and Matthews correlation criteria on the validation set.

It achieves the following results on the evaluation set:
- Loss: 0.0617
- Matthews Correlation: 0.9830

## 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: 3e-05
- train_batch_size: 6
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:-----:|:---------------:|:--------------------:|
| 0.0634        | 1.0   | 20276 | 0.0617          | 0.9830               |


### Framework versions

- Transformers 4.12.3
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3

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transformers pytorch tensorboard bert text-classification generated_from_trainer fa license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

pft-clf-finetuned
=================


This model is a fine-tuned version of HooshvareLab/bert-fa-zwnj-base on an "FarsNews1398" dataset. This dataset contains a collection of news that has been gathered from the farsnews website which is a news agency in Iran. You can download the dataset from here. I used category, abstract, and paragraphs of news for doing text classification. "abstract" and "paragraphs" for each news concatenated together and "category" used as a target for classification.


The notebook used for fine-tuning can be found here. I've reported loss and Matthews correlation criteria on the validation set.


It achieves the following results on the evaluation set:


* Loss: 0.0617
* Matthews Correlation: 0.9830


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: 3e-05
* train\_batch\_size: 6
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1


### Training results



### Framework versions


* Transformers 4.12.3
* Pytorch 1.10.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 6
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.12.3
* Pytorch 1.10.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 6
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.12.3
* Pytorch 1.10.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 6
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1### Training results### Framework versions


* Transformers 4.12.3
* Pytorch 1.10.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3