Create README.md

README.md

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+---
+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
+```python
+
+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)