README.md

---
language:
- ar
metrics:
- Accuracy
- F1_score
- BLEU
library_name: transformers
pipeline_tag: text2text-generation
tags:
- t5
- text2text-generation
- seq2seq
- Classification and Generation
- Classification
- Generation
- ArabicT5
- Text Classification
- Text2Text Generation
widget:
- example_title: "الرياضة"
- text: >
    خسارة مدوية لليفربول امام تولوز وفوز كبير لبيتيس
---

# 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
  }


# Example usage
```python

from transformers import T5ForConditionalGeneration, T5Tokenizer, pipeline

model_name="Hezam/ArabicT5-news-classification-generation-45GB-base"
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"

output= generation_pipeline(text,
                    num_beams=10,
                    max_length=512,
                    top_p=0.9,
                    repetition_penalty = 3.0,
                    no_repeat_ngram_size = 3)[0]["generated_text"]

print(output)