Projects sharing features with Go Ego Gse

Chinese segmentation library

Wukong is a distributed full-text search engine designed for indexing and retrieving text documents. It functions as a customizable search backend that employs a BM25 relevance ranker to order search results based on term frequency and inverse document frequency. The system includes a specialized Chinese text segmenter to break continuous character strings into meaningful words for accurate indexing and retrieval. To handle large datasets and high request volumes, it utilizes a distributed search index that employs hash-based sharding to split documents across multiple nodes. The engine prov

Blast is a full text search and indexing server, written in Go, built on top of Bleve.

This project is a Go client library and API wrapper for interacting with Elasticsearch clusters. It serves as a programmatic interface for managing documents, indices, and cluster health, allowing Go applications to perform search and indexing operations via the REST API. The library functions as a distributed search orchestrator, providing specialized tools for high-throughput data ingestion and cluster administration. It features a buffered bulk processor with exponential backoff retries for optimizing write performance and supports automated index lifecycle transitions and historical data

A tokenizer based on the dictionary and Bigram language models for Go. (Now only support chinese segmentation)

Gorse open source recommender system engine

This project is a Chinese text segmentation library and tokenizer designed to split Chinese sentences into individual words. It serves as a natural language processing tool for splitting characters into words, tagging parts of speech, and extracting keywords using statistical analysis. The library distinguishes itself through support for custom dictionary configuration and vocabulary file management, allowing users to override default segmentation rules for domain-specific accuracy. It also includes a TF-IDF keyword extractor to identify significant words and core topics within documents. Th

A Go library for performing Unicode Text Segmentation as described in Unicode Standard Annex #29

a chinese segment base on crf

The official Go client for Elasticsearch

Riot is a Go-based distributed search engine and indexing server designed for full-text indexing and retrieval. It functions as a retrieval system that sorts documents by relevance using BM25 ranking algorithms, term frequency, and inverse document frequency. The engine provides specialized support for the Chinese language, featuring concurrent text segmentation and phonetic Pinyin mapping to match romanized input with characters. It utilizes a distributed architecture that employs hash-based index sharding to balance data load and throughput across multiple server nodes. The system covers a

Go中文分词

pkuseg-python is a Chinese word segmentation toolkit and natural language processing library. It provides specialized models for splitting Chinese text into words across various domains, including news, medical, and web content, and includes a tool for assigning grammatical parts of speech tags to segmented words. The library allows for the training of custom segmentation models using annotated datasets and supports the integration of user-defined dictionaries to ensure specialized terminology is recognized correctly. It employs a multi-threaded execution engine to process large volumes of Ch

Implementation of various topic models

Chat with your favourite LLaMA models in a native macOS app

Multilingual text (NLP) processing toolkit

lecture notes for probabilistic topic models using ipython notebook

This project is a quantized fine-tuning framework for large language models. It implements a low-rank adaptation library and a four-bit quantizer to reduce the GPU memory requirements needed to train large models. The framework utilizes four-bit quantization and low-rank adapters to enable model training on consumer-grade hardware. It further reduces the memory footprint through double quantization and a paged optimizer that offloads states to system RAM. The system supports distributed training across multiple GPUs to handle larger parameter scales and includes utilities for custom dataset

Argilla is a collaborative AI feedback tool and data curation management system. It serves as a human-in-the-loop dataset platform designed to coordinate workforce annotators and domain experts in labeling, rating, and refining data samples for machine learning projects. The platform focuses on large language model dataset curation and reinforcement learning from human feedback workflows. It provides a shared workspace for integrating human expertise into AI development to validate model outputs and correct data errors. The system manages the end-to-end machine learning data pipeline, includ