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---
language:
- ar
metrics:
- bleu
- accuracy
library_name: transformers
pipeline_tag: text-classification
tags:
- t5
- Classification
- ArabicT5
- Text Classification
widget:
- example_title: >
الديني
- text: >
الحمد لله رب العالمين والصلاة والسلام على سيد المرسلين نبينا محمد وآله وصحبه أجمعين،وبعد:فإنه يجب على العبد أن يتجنب الذنوب كلها دقها وجلها صغيرها وكبيرها وأن يتعاهد نفسه بالتوبة الصادقة والإنابة إلى ربه. قال تعالى: (وَتُوبُوا إِلَى اللَّهِ جَمِيعًا أَيُّهَا الْمُؤْمِنُونَ لَعَلَّكُمْ تُفْلِحُونَ)النور 31.
---
# # Arabic text classification using deep learning (ArabicT5)
- SANAD: Single-label Arabic News Articles Dataset for automatic text categorization
- Paper
[https://www.researchgate.net/publication/333605992_SANAD_Single-Label_Arabic_News_Articles_Dataset_for_Automatic_Text_Categorization]
-Dataset
[https://data.mendeley.com/datasets/57zpx667y9/2]
# # Their experiment'
[https://www.sciencedirect.com/science/article/abs/pii/S0306457319303413]
"Our experimental results showed that all models did very well on SANAD corpus with a minimum accuracy of 93.43%, achieved by CGRU, and top performance of 95.81%, achieved by HANGRU."
| Model | Accuracy |
| :---------------------: | :---------------------: |
| CGRU | 93.43% |
| HANGRU | 95.81% |
# # Our experiment
# # The category mapping
category_mapping = {
'Politics':1,
'Finance':2,
'Medical':3,
'Sports':4,
'Culture':5,
'Tech':6,
'Religion':7
}
# # Training parameters
| | |
| :-------------------: | :-----------:|
| Training batch size | `8` |
| Evaluation batch size | `8` |
| Learning rate | `1e-4` |
| Max length input | `200` |
| Max length target | `3` |
| Number workers | `4` |
| Epoch | `2` |
| | |
# # Results
| | |
| :---------------------: | :-----------: |
| Validation Loss | `0.0479` |
| Accuracy | `96.49%` |
| BLeU | `96.49%` |
# # Example usage
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
model_name="Hezam/ArabicT5_Classification"
model = T5ForConditionalGeneration.from_pretrained(model_name)
tokenizer = T5Tokenizer.from_pretrained(model_name)
text = "الزين فيك القناه الاولي المغربيه الزين فيك القناه الاولي المغربيه اخبارنا المغربيه متابعه تفاجا زوار موقع القناه الاولي المغربي"
tokens=tokenizer(text, max_length=200,
truncation=True,
padding="max_length",
return_tensors="pt"
)
output= model.generate(tokens['input_ids'],
max_length=3,
length_penalty=10)
output = [tokenizer.decode(ids, skip_special_tokens=True,clean_up_tokenization_spaces=True)for ids in output]
output
```
```bash
['5']
``` |