metadata

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


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)