/Text_summarization_using_GPT-J

Text summarization using GPT-J6B offers a powerful and efficient approach to condensing large volumes of text into concise summaries. It can be used in various applications, including information retrieval, content summarization, and document analysis, to help users quickly grasp the key points of lengthy texts.

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GPT-J 6B

GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX. "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters. Model link:

GPT-J-6B, an open-source autoregressive language model developed by the EleutherAI research group, offers a highly advanced alternative to OpenAI's GPT-3. This model demonstrates exceptional performance across various natural language processing tasks. GPT-J, in addition to its impressive performance on a wide range of natural language tasks, notably surpasses GPT-3 in code generation tasks.

It can be used in various applications:

  • Sentiment Analysis
  • code generation
  • Entity Extraction (NER)
  • Question Answering
  • Grammar and Spelling Correction
  • Language Translation
  • Tweet Generation
  • Chatbot and Conversational AI
  • Intent Classification
  • Paraphrasing
  • Summarization
  • Keyword and Keyphrase Extraction
  • Product Description and Ad Generation
  • Blog Post Generation