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README.md
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/645817bb72b60ae7a37f8f40/6gZDjcAOhWLvN5xF-E2FE.png)
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# Example usage
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from
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model = T5ForConditionalGeneration.from_pretrained(model_name)
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tokenizer = T5Tokenizer.from_pretrained(model_name)
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/645817bb72b60ae7a37f8f40/6gZDjcAOhWLvN5xF-E2FE.png)
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# Example usage
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from transformers import T5ForConditionalGeneration, T5Tokenizer, pipeline
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from arabert.preprocess import ArabertPreprocessor
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arabert_prep = ArabertPreprocessor(model_name="aubmindlab/bert-base-arabertv2")
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model_name="Hezam/arabic-T5-news-classification-generation"
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model = T5ForConditionalGeneration.from_pretrained(model_name)
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tokenizer = T5Tokenizer.from_pretrained(model_name)
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generation_pipeline = pipeline("text2text-generation",model=model,tokenizer=tokenizer)
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text = " الاستاذ حزام جوبح يحصل على براعة اختراع في التعلم العميق"
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text_clean = arabert_prep.preprocess(text)
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g=generation_pipeline(text_clean,
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num_beams=10,
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max_length=config.Generation_LEN,
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top_p=0.9,
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repetition_penalty = 3.0,
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no_repeat_ngram_size = 3)[0]["generated_text"]
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