tanalpha-aditya/Prompt-Engineering-BERT-NLP
Implement prompt tuning on a GPT-2 small model using PyTorch and fine-tune it on three tasks: summarization, question answering, and machine translation.
This project helps AI developers and researchers efficiently adapt large language models for specific tasks without extensive retraining. By providing examples and code, it demonstrates how to use prompt tuning to achieve summarization, question answering, and machine translation with a GPT-2 model. The end-user is a machine learning practitioner who wants to fine-tune existing models.
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Use this if you are a machine learning engineer or researcher looking for practical examples of prompt tuning for NLP tasks like summarization, QA, or translation.
Not ideal if you are a non-technical user seeking a ready-to-use application, or if you require a comprehensive guide to prompt engineering theory rather than implementation examples.
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Jupyter Notebook
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Last pushed
Dec 07, 2023
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