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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- matthews_correlation |
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model-index: |
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- name: pft-clf-finetuned |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# pft-clf-finetuned |
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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. |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0617 |
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- Matthews Correlation: 0.9830 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 6 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------------------:| |
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| 0.0634 | 1.0 | 20276 | 0.0617 | 0.9830 | |
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### Framework versions |
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- Transformers 4.12.3 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.15.1 |
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- Tokenizers 0.10.3 |
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