---
language:
- ar
metrics:
- bleu
- accuracy
library_name: transformers
pipeline_tag: text-classification
tags:
- t5
- Classification
- ArabicT5
- Text Classification
widget:
- example_title: الثقافي
- text: >
    الزين فيك القناه الاولي المغربيه الزين فيك القناه الاولي المغربيه اخبارنا
    المغربيه  متابعه تفاجا زوار موقع القناه الاولي المغربي
---

# # Arabic text classification using deep learning (ArabicT5)
  - SANAD: Single-label Arabic News Articles Dataset for automatic text categorization
  [https://www.researchgate.net/publication/333605992_SANAD_Single-Label_Arabic_News_Articles_Dataset_for_Automatic_Text_Categorization]
  [https://data.mendeley.com/datasets/57zpx667y9/2]

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   |    `128`     |
|   Max length target   |     `3`      |
|     Number workers    |     `4`      |
|         Epoch         |     `2`      |
|                       |              |

# # Results

|                         |               |
| :---------------------: | :-----------: | 
|   Validation Loss       |   `0.0479`    |  
|        Accuracy         |    `96.%`     | 
|          BLeU           |     `96%`     |

# # Example usage
```python

from transformers import T5ForConditionalGeneration, T5Tokenizer, pipeline

model_name = "Hezam/ArabicT5_Classification"
model = T5ForConditionalGeneration.from_pretrained(model_name)
tokenizer = T5Tokenizer.from_pretrained(model_name)
generation_pipeline = pipeline("text-classification",model=model,tokenizer=tokenizer)

text = "الزين فيك القناه الاولي المغربيه الزين فيك القناه الاولي المغربيه اخبارنا المغربيه  متابعه تفاجا زوار موقع القناه الاولي المغربي"
output= generation_pipeline(text,
                    num_beams=10,
                    max_length=3,
                    top_p=0.9,
                    repetition_penalty = 3.0,
                    no_repeat_ngram_size = 3)

output

```bash
5
```