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--- |
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language: |
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- ar |
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metrics: |
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- accuracy |
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- bleu |
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library_name: transformers |
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pipeline_tag: text2text-generation |
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--- |
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This model is under trial. |
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The number in the generated text represents the category of the news, as shown below. |
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category_mapping = { |
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'Political':1, |
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'Economy':2, |
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'Health':3, |
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'Sport':4, |
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'Culture':5, |
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'Technology':6, |
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'Art':7, |
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'Accidents':8 |
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} |
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# Example usage |
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model_name = "Hezam/arabic-T5-news-classification-generation" |
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from transformers import T5ForConditionalGeneration, T5Tokenizer |
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model = T5ForConditionalGeneration.from_pretrained(model_name) |
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tokenizer = T5Tokenizer.from_pretrained(model_name) |
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input_text = " الاستاذ حزام جوبح يحصل على براعة اختراع في التعلم العميق" |
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output_text = model.generate(input_text) |
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print(generated_text) |
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