|
--- |
|
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 |
|
model_name = "Hezam/arabic-T5-news-classification-generation" |
|
from transformers import T5ForConditionalGeneration, T5Tokenizer |
|
model = T5ForConditionalGeneration.from_pretrained(model_name) |
|
tokenizer = T5Tokenizer.from_pretrained(model_name) |
|
input_text = " الاستاذ حزام جوبح يحصل على براعة اختراع في التعلم العميق" |
|
|
|
output_text = model.generate(input_text) |
|
|
|
print(generated_text) |
|
|
|
|
