Tags: hugging face transformers*

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  1. In this post, we'll explore how to use Hugging Face's Pipeline API to generate summaries with a zero-shot model and train a summarization model on the arXiv dataset. We'll also evaluate the trained model and compare it to the simple heuristic we developed in the previous post.

  2. This article provides a beginner's guide on using Hugging Face Transformers for text summarization. It explains what text summarization is, its uses, and how it can be performed using extractive and abstractive summarization techniques. The article also provides a simple code example using the Hugging Face pipeline for text summarization.

  3. txtai is an open-source embeddings database for various applications such as semantic search, LLM orchestration, language model workflows, and more. It allows users to perform vector search with SQL, create embeddings for text, audio, images, and video, and run pipelines powered by language models for question-answering, transcription, translation, and more.

  4. • A beginner's guide to understanding Hugging Face Transformers, a library that provides access to thousands of pre-trained transformer models for natural language processing, computer vision, and more. • The guide covers the basics of Hugging Face Transformers, including what it is, how it works, and how to use it with a simple example of running Microsoft's Phi-2 LLM in a notebook • The guide is designed for non-technical individuals who want to understand open-source machine learning without prior knowledge of Python or machine learning.

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