A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization. - google/langextract
Google for Developers Blog - News about Web, Mobile, AI and Cloud
Explore LangExtract: a Gemini-powered, open-source Python library for reliable, structured information extraction from unstructured text with precise source grounding.
Natural Language Processing With Python's NLTK Package – Real Python
In this beginner-friendly tutorial, you'll take your first steps with Natural Language Processing (NLP) and Python's Natural Language Toolkit (NLTK). You'll learn how to process unstructured data in order to be able to analyze it and draw conclusions from it.
Structured LLM output from ATProto Lexicons
Generating 3D Meshes From Text
I recently had a desire to convert text to 3D meshes that I could render and manipulate as part of my Geotoy project and Geoscript language. I did some research into tools and libraries that could solve different pieces of this, and I put together a pipeline that implements the whole thing - yielding nice, 2-manifold 3D meshes with arbitrary fonts, text styles, and more.
Living documents and interactive schematics — Selin Jessa
Using LLMs to turn static figures from data analysis, schematics, and publications into interactive widgets as tools for thought
blaine/relationaltext
WASM-backed rich text library with flat facets, 40+ format adapters, and a lens system for cross-format translation
A new way to extract detailed transcripts from Claude Code
I’ve released claude-code-transcripts, a new Python CLI tool for converting Claude Code transcripts to detailed HTML pages that provide a better interface for understanding what Claude Code has done than …
10 Easy Ways to Enhance Your LLM Wiki or Knowledge Base
DevReal: Simple Knowledge Graphs with Outlines, neo4j, and Modal, Cameron Pfiffer
Schema Induction using LLMs | Sumit Agarwal
Inducing patterns from articles using LLMs
Unsloth Docs | Unsloth Documentation
Unsloth is an open-source framework for running and training LLMs.
Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus
Large language models have led to remarkable progress on many NLP tasks, and researchers are turning to ever-larger text corpora to train them. Some of the largest corpora available are made by scraping significant portions of the internet, and are frequently introduced with only minimal documentation. In this work we provide some of the first documentation for the Colossal Clean Crawled Corpus (C4; Raffel et al., 2020), a dataset created by applying a set of filters to a single snapshot of Common Crawl. We begin by investigating where the data came from, and find a significant amount of text from unexpected sources like patents and US military websites. Then we explore the content of the text itself, and find machine-generated text (e.g., from machine translation systems) and evaluation examples from other benchmark NLP datasets. To understand the impact of the filters applied to create this dataset, we evaluate the text that was removed, and show that blocklist filtering disproportionately removes text from and about minority individuals. Finally, we conclude with some recommendations for how to created and document web-scale datasets from a scrape of the internet.
