Search Results for "natural language processing" - Page 2
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Showing 1439 open source projects for "natural language processing"
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spaCy
Industrial-strength Natural Language Processing (NLP)
spaCy is a library built on the very latest research for advanced Natural Language Processing (NLP) in Python and Cython. Since its inception it was designed to be used for real world applications-- for building real products and gathering real insights. It comes with pretrained statistical models and word vectors, convolutional neural network models, easy deep learning integration and so much more. spaCy is the fastest syntactic parser in the world according to independent benchmarks, with an accuracy within 1% of the best available. ... -
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Search-Index
A persistent, network resilient, full text search library
Search-Index is a lightweight and fast JavaScript-based search engine that enables full-text search indexing and retrieval for web applications. -
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Pyreft
ReFT: Representation Finetuning for Language Models
PyreFT is a tool by Stanford NLP for fine-tuning transformer models with an emphasis on efficient, resource-conserving training and customizability for NLP tasks. -
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SciSpaCy
A full spaCy pipeline and models for scientific/biomedical documents
ScispaCy is a spaCy extension optimized for processing biomedical and scientific text, providing domain-specific NLP models for tasks like named entity recognition (NER) and dependency parsing. -
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BettaFish
Public opinion analysis system
...It uses a modular architecture of specialized agents that collaborate to crawl mainstream platforms, extract multimodal content like text and short video, and synthesize insights through both statistical and large language model techniques. With a design that lets users pose questions in natural language and receive structured reports, charts, and visualizations, the system aims to break information cocoons and provide comprehensive views of trends and public sentiment. Unlike simpler analytics tools, BettaFish employs agent collaboration and a “forum” style internal mechanism to combine diverse model outputs, making the analysis richer and more robust. ... -
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OpenVINO
OpenVINO™ Toolkit repository
OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference. Boost deep learning performance in computer vision, automatic speech recognition, natural language processing and other common tasks. Use models trained with popular frameworks like TensorFlow, PyTorch and more. Reduce resource demands and efficiently deploy on a range of Intel® platforms from edge to cloud. This open-source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. ... -
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ChatGLM.cpp
C++ implementation of ChatGLM-6B & ChatGLM2-6B & ChatGLM3 & GLM4(V)
ChatGLM.cpp is a C++ implementation of the ChatGLM-6B model, enabling efficient local inference without requiring a Python environment. It is optimized for running on consumer hardware. -
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Text Generation Inference
Large Language Model Text Generation Inference
Text Generation Inference is a high-performance inference server for text generation models, optimized for Hugging Face's Transformers. It is designed to serve large language models efficiently with optimizations for performance and scalability. -
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Open Interpreter
A natural language interface for computers
Open Interpreter is an open-source tool that provides a natural-language interface for interacting with your computer. It lets large language models (LLMs) run code locally (Python, JavaScript, shell, etc.), enabling you to ask your computer to do tasks like data analysis, file manipulation, browsing, etc. in human terms (“chat with your computer”), with safeguards. Runs locally or via configured remote LLM servers/inference backends, giving flexibility to use models you trust or have locally. ... -
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ExtractThinker
ExtractThinker is a Document Intelligence library for LLMs
ExtractThinker is a tool designed to facilitate the extraction and analysis of information from various data sources, aiding in data processing and knowledge discovery. -
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Apache OpenNLP
Apache OpenNLP
Apache OpenNLP is a machine learning-based NLP library that provides tools for text-processing tasks such as tokenization, sentence segmentation, and named entity recognition. -
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Chinese-XLNet
Chinese XLNet pre-trained model
Chinese-XLNet is a Chinese language pre-trained model based on the XLNet architecture, providing an advanced foundation for natural language processing tasks in Mandarin and other Chinese dialects. Unlike traditional masked language modeling, XLNet uses a permutation language modeling objective that captures bidirectional context more effectively by training over all possible token orderings, yielding richer contextual representations. ... -
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Ring Programming Language
Simple and flexible programming language for applications development
Simple and flexible programming language for applications development. Ring is a practical general-purpose multi-paradigm language. The supported programming paradigms are Imperative, Procedural, Object-Oriented, Functional, Metaprogramming, Declarative programming using nested structures, and Natural programming. The language is portable (MS-DOS, Windows, Linux, macOS, Android, WebAssembly, etc.) and can be used to create Console, GUI, Web, Games, and Mobile applications. -
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deepdoctection
A Repo For Document AI
DeepDoctection is a document AI framework that applies deep learning techniques to analyze and extract structured data from scanned documents, PDFs, and images. deepdoctection is a Python library that orchestrates document extraction and document layout analysis tasks using deep learning models. It does not implement models but enables you to build pipelines using highly acknowledged libraries for object detection, OCR and selected NLP tasks and provides an integrated frameworks for... -
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LLM.swift
LLM.swift is a simple and readable library
LLM.swift is a Swift package that enables developers to run Large Language Models (LLMs) directly on Apple devices, including iOS, macOS, and watchOS. By leveraging Apple's hardware and software optimizations, LLM.swift facilitates on-device natural language processing tasks, ensuring user privacy and reducing latency associated with cloud-based solutions. -
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DataDreamer
DataDreamer: Prompt. Generate Synthetic Data. Train & Align Models
DataDreamer is a tool designed to assist in the generation and manipulation of synthetic data for various applications, including testing and machine learning. -
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TextWorld
TextWorld is a sandbox learning environment for the training
TextWorld is a learning environment designed to train reinforcement learning agents to play text-based games, where actions and observations are entirely in natural language. Developed by Microsoft Research, TextWorld focuses on language understanding, planning, and interaction in complex, narrative-driven environments. It generates games procedurally, enabling scalable testing of agents’ natural language processing and decision-making abilities. -
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Stanford CoreNLP
Stanford CoreNLP, a Java suite of core NLP tools
CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations. CoreNLP currently supports 6 languages, Arabic, Chinese, English, French, German, and Spanish. -
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BEIR
A Heterogeneous Benchmark for Information Retrieval
BEIR is a benchmark framework for evaluating information retrieval models across various datasets and tasks, including document ranking and question answering. -
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FastRAG
Efficient Retrieval Augmentation and Generation Framework
fastRAG is a research framework for efficient and optimized retrieval augmented generative pipelines, incorporating state-of-the-art LLMs and Information Retrieval. fastRAG is designed to empower researchers and developers with a comprehensive tool set for advancing retrieval augmented generation. -
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Keras Hub
Pretrained model hub for Keras 3
Keras Hub is a repository of pre-trained models for Keras 3, offering a collection of ready-to-use models for various machine-learning tasks. KerasHub is an extension of the core Keras API; KerasHub components are provided as Layer and Model implementations. If you are familiar with Keras, congratulations. You already understand most of KerasHub. -
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Super comprehensive deep learning notes
Super Comprehensive Deep Learning Notes
Super comprehensive deep learning notes is a massive and well-structured collection of deep learning notebooks that serve as a comprehensive study resource for anyone wanting to learn or reinforce concepts in computer vision, natural language processing, deep learning architectures, and even large-model agents. The repository contains hundreds of Jupyter notebooks that are richly annotated and organized by topic, progressing from basic Python and PyTorch fundamentals to advanced neural network designs like ResNet, transformers, and object detection algorithms. ... -
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NVIDIA NeMo
Toolkit for conversational AI
NVIDIA NeMo, part of the NVIDIA AI platform, is a toolkit for building new state-of-the-art conversational AI models. NeMo has separate collections for Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS) models. Each collection consists of prebuilt modules that include everything needed to train on your data. Every module can easily be customized, extended, and composed to create new conversational AI model architectures. Conversational AI architectures are typically large and require a lot of data and compute for training. ... -
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Datasets
Hub of ready-to-use datasets for ML models
Datasets is a library for easily accessing and sharing datasets, and evaluation metrics for Natural Language Processing (NLP), computer vision, and audio tasks. Load a dataset in a single line of code, and use our powerful data processing methods to quickly get your dataset ready for training in a deep learning model. Backed by the Apache Arrow format, process large datasets with zero-copy reads without any memory constraints for optimal speed and efficiency. ... -
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autoMate
AI tool for automating desktop tasks via natural language input
autoMate is an AI-powered local automation tool designed to enable users to control and automate their computers using natural language instructions instead of traditional scripting or rule-based systems. It combines large language models with computer vision techniques to interpret user intent and understand on-screen content, allowing it to interact with graphical interfaces similarly to a human user. autoMate follows an observe-decide-act workflow, where it analyzes the screen, plans actions, and executes them through simulated input such as mouse clicks and keyboard events. ...
