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---
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
}
# 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"]
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