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README.md
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language:
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- ar
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metrics:
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- accuracy
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- bleu
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pipeline_tag: text-classification
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---
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category_mapping = {
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'Politics':1,
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'Finance':2,
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'Culture':5,
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'Tech':6,
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'Religion':7
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}
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language:
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- ar
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metrics:
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- bleu
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- accuracy
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library_name: transformers
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pipeline_tag: text-classification
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tags:
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- t5
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- Classification
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- ArabicT5
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- Text Classification
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widget:
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- example_title: الثقافي
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- text: >
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الزين فيك القناه الاولي المغربيه الزين فيك القناه الاولي المغربيه اخبارنا
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المغربيه متابعه تفاجا زوار موقع القناه الاولي المغربي
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---
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# # Arabic text classification using deep learning (ArabicT5)
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- SANAD: Single-label Arabic News Articles Dataset for automatic text categorization
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[https://www.researchgate.net/publication/333605992_SANAD_Single-Label_Arabic_News_Articles_Dataset_for_Automatic_Text_Categorization]
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[https://data.mendeley.com/datasets/57zpx667y9/2]
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category_mapping = {
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'Politics':1,
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'Finance':2,
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'Culture':5,
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'Tech':6,
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'Religion':7
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}
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# # Training parameters
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| :-------------------: | :-----------:|
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| Training batch size | `8` |
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| Evaluation batch size | `8` |
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| Learning rate | `1e-4` |
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| Max length input | `128` |
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| Max length target | `3` |
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| Number workers | `4` |
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| Epoch | `2` |
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# # Results
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| :---------------------: | :-----------: |
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| Validation Loss | `0.0479` |
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| Accuracy | `96.%` |
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| BLeU | `96%` |
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# # Example usage
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```python
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from transformers import T5ForConditionalGeneration, T5Tokenizer, pipeline
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model_name = "Hezam/ArabicT5_Classification"
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model = T5ForConditionalGeneration.from_pretrained(model_name)
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tokenizer = T5Tokenizer.from_pretrained(model_name)
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generation_pipeline = pipeline("text-classification",model=model,tokenizer=tokenizer)
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text = "الزين فيك القناه الاولي المغربيه الزين فيك القناه الاولي المغربيه اخبارنا المغربيه متابعه تفاجا زوار موقع القناه الاولي المغربي"
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output= generation_pipeline(text,
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num_beams=10,
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max_length=3,
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top_p=0.9,
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repetition_penalty = 3.0,
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no_repeat_ngram_size = 3)
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output
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```bash
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5
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```
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