README.md

---
language:
- ar
metrics:
- accuracy
- bleu
library_name: transformers
pipeline_tag: text2text-generation
---
This 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
}

![image/png](https://cdn-uploads.huggingface.co/production/uploads/645817bb72b60ae7a37f8f40/6gZDjcAOhWLvN5xF-E2FE.png)

# Example usage
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 = " الاستاذ حزام جوبح يحصل على براعة اختراع في التعلم العميق"
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"]