GitHub - alibaba/data-juicer: A one-stop data processing system to make data higher-quality, juicier, and more digestible for LLMs! 🍎 🍋 🌽 ➡️ ➡️🍸 🍹 🍷为大语言模型提供更高质量、更丰富、更易”消化“的数据！

Original creator : Jesse Zhang (GH: emptycrown, Twitter: @thejessezhang), who courteously donated the repo to LlamaIndex!

This is a simple library of all the data loaders / readers / tools / llama-packs that have been created by the community. The goal is to make it extremely easy to connect large language models to a large variety of knowledge sour... See more

一款适合需要处理大量非结构化AI数据的工具：datachain，支持多模态API调用和本地AI推理并行处理 可以用它结合大模型一起使用，来完成复杂的数据分析任务，比如做，数据处理和清洗、LLM分析和验证、图像分割等 支持图片、视频、文本、PDF、JSON、CSV、parquet等智能统一管理，自动保存处理记录和版本 可以直接从各种云存储，比如谷歌云、亚马逊云或者本地读取数据，无需手动复制 支持智能搜索和分析，支持超大数据集处理、并行处理 github：https://t.co/bJ9rKKuMBY... See more

DataTrove

DataTrove is a library to process, filter and deduplicate text data at a very large scale. It provides a set of prebuilt commonly used processing blocks with a framework to easily add custom functionality.

DataTrove processing pipelines are platform-agnostic, running out of the box locally or on a slurm cluster. Its (relatively) low memory... See more

Clean & curate your data with LLMs

databonsai is a Python library that uses LLMs to perform data cleaning tasks.

Features

• Suite of tools for data processing using LLMs including categorization, transformation, and extraction


• Validation of LLM outputs


• Batch processing for token savings


• Retry logic with exponential backoff for handling rate limits an

... See more

uniflow provides a unified LLM interface to extract and transform and raw documents.

• Document types: Uniflow enables data extraction from PDFs, HTMLs and TXTs.


• LLM agnostic: Uniflow supports most common-used LLMs for text tranformation, including
  
  
  • OpenAI models (GPT3.5 and GPT4),
  
  
  • Google Gemini models (Gemini 1.5, MultiModal),
  
  
  • AWS BedRock models,
  
  
  • Huggingf

... See more

To train LLMs, you need data that is:

Large — Sufficiently large LMs require trillions of tokens.

Clean — Noisy data reduces performance.

Diverse — Data should come from different sources and different knowledge bases.

What does clean data look like?

You can de-duplicate data with simple heuristics. The most basic would be removing any exact duplicates ... See more