README.md

---
language:
- ar
metrics:
- Accuracy
- F1_score
- BLEU
library_name: transformers
pipeline_tag: text2text-generation
tags:
- Classification and Generation
- Classification
- Generation
- ArabicT5
- Text Classification
- Text2Text Generation
widget:
- text: >-
    خسارة مدوية لليفربول امام تولوز وفوز كبير لبيتيس، انتصار الفيولا واستون فيلا
    في دوري المؤتمر، والد لويس دياز حر، فوز انديانا على ميلووكي, انتصار
    للانترانيك
---

# ArabicT5: Classification and Generation of Arabic News
  - The model is under trial

# The number in the generated text represents the category of the news, as shown below:
  category_mapping = {
  
      'Political':1,
      'Economy':2,
      'Health':3,
      'Sport':4,
      'Culture':5,
      'Technology':6,
      'Art':7,
      'Accidents':8
  }

## Pre-training Settings and Results on TyDi QA Development Dataset ( Model in this card is highlighted in bold )

|     Name         |     Type     |     Value   |    Verified   |
|------------------|--------------|-------------|---------------|
| Accuracy         |   accuracy   |    96.67%   |     true      | 
| F1_score         |   f1_score   |    96.67%   |     true      |       
| BLEU             |    bleu      |    96.23%   |     true      | 
| Loss             |    loss      |0.57164502143|     true      | 


# Example usage
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer, pipeline
from arabert.preprocess import ArabertPreprocessor

arabert_prep = ArabertPreprocessor(model_name="aubmindlab/bert-base-arabertv2")
model_name="Hezam/arabic-T5-news-classification-generation"
model = T5ForConditionalGeneration.from_pretrained(model_name)
tokenizer = T5Tokenizer.from_pretrained(model_name)
generation_pipeline = pipeline("text2text-generation",model=model,tokenizer=tokenizer)

text = " خسارة مدوية لليفربول امام تولوز وفوز كبير لبيتيس، انتصار الفيولا واستون فيلا في دوري المؤتمر، والد لويس دياز حر، فوز انديانا على ميلووكي, انتصار للانترانيك"

text_clean = arabert_prep.preprocess(text)
g=generation_pipeline(text_clean,
                    num_beams=10,
                    max_length=config.Generation_LEN,
                    top_p=0.9,
                    repetition_penalty = 3.0,
                    no_repeat_ngram_size = 3)[0]["generated_text"]
```

```bash
output: 
```