5 dépôts
Distributing linguistic parsing tasks across multiple CPU cores to increase throughput.
Distinct from Text Processing and Parsing: Candidates focused on visual parallel reading or AST caching, not multi-threaded parsing execution.
Explore 5 awesome GitHub repositories matching software engineering & architecture · Parallel Text Processing. Refine with filters or upvote what's useful.
xi-editor is a high-performance text editor core written in Rust. It employs a client-server architecture that separates the backend editor logic from the user interface, allowing diverse frontends to communicate with the core via a standardized protocol. The project is distinguished by its use of rope-based text buffers for efficient manipulation of large documents and a collaborative engine powered by conflict-free replicated data types to synchronize concurrent edits. It further features an extensible plugin system that integrates external binaries and third-party tools through JSON-based
Distributes expensive text operations like word wrapping across multiple CPU cores using a MapReduce approach.
Compromise is a natural language processing library and rule-based text parser designed to analyze unstructured text. It functions as a toolkit for identifying parts of speech, linguistic patterns, and semantic meaning, while providing specialized engines for named entity recognition and the parsing of temporal and numeric data. The project is distinguished by its linguistic morphological engine, which can conjugate verbs across different tenses and inflect nouns and adjectives. It further allows for linguistic model customization through a plugin system that enables the extension of lexicons
Distributes sentence parsing across multiple worker threads to process large volumes of text efficiently.
This project is a high-performance library for converting raw text into tokens and IDs for machine learning models. It functions as a fast text encoder and a text preprocessing pipeline designed to transform strings into numerical representations with high throughput for research and production. The library includes a subword tokenizer trainer used to analyze text datasets and create custom vocabularies using algorithms such as byte-pair encoding and wordpiece. It provides capabilities for subword vocabulary training and text alignment, allowing character offsets to be tracked during normaliz
Implements multi-threaded parallel processing of text to increase encoding throughput across multiple CPU cores.
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
Distributes word segmentation tasks across multiple CPU threads to increase processing throughput.
LAC is a Chinese lexical analysis engine and toolkit designed for joint word segmentation, part-of-speech tagging, and named entity recognition. It functions as a high-performance system that identifies word boundaries and grammatical categories using trained machine learning models. The project features a lightweight, compiled native runtime that enables on-device natural language processing and embedding into mobile applications. It includes model compression and conversion to optimize for resource-constrained environments and supports multi-threaded parallel execution to increase throughpu
Supports multi-threaded parallel execution to distribute text processing tasks across CPU cores.