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| Text2Text Generation | |
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| Arabic | |
| arxiv:2109.12068 | |
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| Arabic_Question_Generation | |
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| README.md | |
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| Mihakram | |
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| --- | |
| language: | |
| - ar | |
| widget: | |
| - text: "context: الثورة الجزائرية أو ثورة المليون شهيد، اندلعت في 1 نوفمبر 1954 ضد المستعمر الفرنسي ودامت 7 سنوات ونصف. استشهد فيها أكثر من مليون ونصف مليون جزائري answer: 7 سنوات ونصف </s> | |
| " | |
| - text: "context: اسكتلندا دولة في شمال غرب أوروبا، تعتبر جزء من الدول الأربع المكونة المملكة المتحدة. تحتل الثلث الشمالي من جزيرة بريطانيا العظمى وتحدها جنوبا إنجلترا ويحدها شرقا بحر الشمال وغربا المحيط الأطلسي. عاصمتها أدنبرة، وأهم مدنها وأكبرها مدينة غلاسكو. كانت اسكتلندا مملكة مستقلة حتى 1 مايو 1707 answer: أدنبرة </s>" | |
| - text: "context: مات المستشار الألماني أدولف هتلر في 30 أبريل 1945 منتحرا عن طريق تناول مادة السيانيد السامة وإطلاق النار على نفسه وهي الرواية العامة المقبولة لطريقة موت الزعيم النازي answer: منتحرا </s> | |
| " | |
| --- | |
| # Arabic Question generation Model | |
| This model is ready to use for **Question generation**, simply input the text and answer and the model will generate a question, This model is a fine-tuned version of [AraT5-Base Model](https://huggingface.co/UBC-NLP/AraT5-base) | |
| ## Requirements | |
| ``` | |
| !pip install transformers | |
| ``` | |
| ## Model in Action 🚀 | |
| ```python | |
| from transformers import AutoTokenizer,AutoModelForSeq2SeqLM | |
| model = AutoModelForSeq2SeqLM.from_pretrained("Mihakram/Arabic_Question_Generation") | |
| tokenizer = AutoTokenizer.from_pretrained("Mihakram/Arabic_Question_Generation") | |
| def get_question(context,answer): | |
| text="context: " +context + " " + "answer: " + answer + " </s>" | |
| text_encoding = tokenizer.encode_plus( | |
| text,return_tensors="pt" | |
| ) | |
| model.eval() | |
| generated_ids = model.generate( | |
| input_ids=text_encoding['input_ids'], | |
| attention_mask=text_encoding['attention_mask'], | |
| max_length=64, | |
| num_beams=5, | |
| num_return_sequences=1 | |
| ) | |
| return tokenizer.decode(generated_ids[0],skip_special_tokens=True,clean_up_tokenization_spaces=True).replace('question: ',' ') | |
| context="الثورة الجزائرية أو ثورة المليون شهيد، اندلعت في 1 نوفمبر 1954 ضد المستعمر الفرنسي ودامت 7 سنوات ونصف. استشهد فيها أكثر من مليون ونصف مليون جزائري" | |
| answer =" 7 سنوات ونصف" | |
| get_question(context,answer) | |
| #output : question="كم استمرت الثورة الجزائرية؟ " | |
| ``` | |
| ## Expriments | |
| We report score with `NQG Scorer`. | |
| If not special explanation, the size of the model defaults to "base". | |
| ### Metrics resaults | |
| Bleu 1|Bleu 2|Bleu 3|Bleu 4|METEOR|ROUGE-L| | |
| ------|------|------|------|------|-------| | |
| 54.67 |39.26 |30.34 |24.15 |25.43 |52.64 | | |
| ## References | |
| The **Ara-T5** model was presented in [AraT5: Text-to-Text Transformers for Arabic Language Generation](https://arxiv.org/abs/2109.12068) by *El Moatez Billah Nagoudi, AbdelRahim Elmadany, Muhammad Abdul-Mageed* | |
| ## Citation | |
| If you want to cite this model you can use this: | |
| ```bibtex | |
| @misc{Mihakram/, | |
| title={}, | |
| author={Mihoubi, Ibrir}, | |
| publisher={Hugging Face}, | |
| journal={Hugging Face Hub}, | |
| howpublished={\url{https://huggingface.co/}}, | |
| year={2022} | |
| } | |
| ``` |