Popular GitHub Repositories for Learned Chunking

? CHONK your texts with Chonkie ? - The no-nonsense RAG chunking library

NCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.

? RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite

Implementation of MEGABYTE, Predicting Million-byte Sequences with Multiscale Transformers, in Pytorch

The nnsight package enables interpreting and manipulating the internals of deep learned models.

TensorFlow Implementation of Generative Adversarial Networks for Extreme Learned Image Compression

Object detection on multiple datasets with an automatically learned unified label space.

AI Image Signal Processing and Computational Photography. Official library for NTIRE (CVPR) and AIM (ICCV/ECCV) Challenges. You will find Learned ISPs, RAW Restoration-Upsampling-Reconstruction, Image Enhancement, Bokeh rendering and more!

Fully neural approach for text chunking

A fast, lightweight and easy-to-use Python library for splitting text into semantically meaningful chunks.

Deep learned, NVIDIA-accelerated 3D object pose estimation

Multi-hop knowledge graph reasoning learned via policy gradient with reward shaping and action dropout

A package for parsing PDFs and analyzing their content using LLMs.

The RAG Experiment Accelerator is a versatile tool designed to expedite and facilitate the process of conducting experiments and evaluations using Azure Cognitive Search and RAG pattern.

? CHONK your texts with Chonkie ✨ Type-friendly, light-weight, fast and super-simple chunking library

LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks

A TensorFlow implementation of Neural Sequence Labeling model, which is able to tackle sequence labeling tasks such as POS Tagging, Chunking, NER, Punctuation Restoration and etc.

? GUI for editing LLM vector embeddings. No more blind chunking. Upload content in any file extension, join and split chunks, edit metadata and embedding tokens + remove stop-words and punctuation with one click, add images, and download in .veml to share it with your team.

Official implementation of "Accelerating Reinforcement Learning with Learned Skill Priors", Pertsch et al., CoRL 2020

pretrained BERT model for cyber security text, learned CyberSecurity Knowledge