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18 lines
2.0 KiB
Markdown
18 lines
2.0 KiB
Markdown
# BERTology
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يُشهد في الآونة الأخيرة نمو مجال دراسي يُعنى باستكشاف آلية عمل نماذج المحولات الضخمة مثل BERT (والذي يُطلق عليها البعض اسم "BERTology"). ومن الأمثلة البارزة على هذا المجال ما يلي:
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- BERT Rediscovers the Classical NLP Pipeline بواسطة Ian Tenney و Dipanjan Das و Ellie Pavlick:
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https://huggingface.co/papers/1905.05950
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- Are Sixteen Heads Really Better than One? بواسطة Paul Michel و Omer Levy و Graham Neubig: https://huggingface.co/papers/1905.10650
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- What Does BERT Look At? An Analysis of BERT's Attention بواسطة Kevin Clark و Urvashi Khandelwal و Omer Levy و Christopher D.
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Manning: https://huggingface.co/papers/1906.04341
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- CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure: https://huggingface.co/papers/2210.04633
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لإثراء هذا المجال الناشئ، قمنا بتضمين بعض الميزات الإضافية في نماذج BERT/GPT/GPT-2 للسماح للناس بالوصول إلى التمثيلات الداخلية، والتي تم تكييفها بشكل أساسي من العمل الرائد لـ Paul Michel (https://huggingface.co/papers/1905.10650):
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- الوصول إلى جميع الحالات المخفية في BERT/GPT/GPT-2،
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- الوصول إلى جميع أوزان الانتباه لكل رأس في BERT/GPT/GPT-2،
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- استرجاع قيم ومشتقات مخرجات الرأس لحساب درجة أهمية الرأس وحذفه كما هو موضح في https://huggingface.co/papers/1905.10650.
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ولمساعدتك على فهم واستخدام هذه الميزات بسهولة، أضفنا مثالًا برمجيًا محددًا: [bertology.py](https://github.com/huggingface/transformers-research-projects/tree/main/bertology/run_bertology.py) أثناء استخراج المعلومات وتقليص من نموذج تم تدريبه مسبقًا على GLUE. |