#NLP

This extensive repository offers a curated collection of open-source GitHub packages focused on Chinese NLP. Covering topics like ChatGPT model evaluations, multi-modal models, and domain applications, it serves as a comprehensive toolkit. The repository is designed for ease of access and encourages community contributions with regular updates.

Discover a structured collection of NLP models and methods, covering essentials from TF-IDF to modern Transformer and BERT approaches. This tutorial details fundamental NLP concepts including Word2Vec, Seq2Seq, and attention models, enhanced by practical code examples and illustrations. Suitable for those aiming to expand their expertise in NLP frameworks and practical applications. Learn about straightforward installation guides and efforts to streamline intricate NLP models via Keras and PyTorch.

This guide provides essential resources to learn Large Language Models (LLMs) without needing an advanced background. Stay informed with the latest updates, techniques, and innovations in 2024 while accessing free resources like tutorials, courses, and community forums. Develop skills in areas such as Transformers and NLP through practical exercises and clear explanations. Suitable for all learning styles, the guide enables learners to become proficient in LLMs independently.

Explore core NLP concepts and models such as Word2Vec and LDA, alongside practical uses like sentiment analysis. See how NLP improves textual comprehension in tasks like spam detection and document classification. This GitHub-updated open-source resource provides flexibility and timely insights in the rapidly advancing NLP field.

UER-py is a toolkit for pre-training and fine-tuning NLP models on general-domain corpora and downstream tasks. It features a modular architecture supporting models such as BERT and GPT-2, facilitating the extension and utilization of pre-trained models from its model zoo. Achieving high performance in tasks like classification and reading comprehension, UER-py is compatible with CPU and multi-GPU systems, offering comprehensive functions for researchers to explore and optimize advanced models.

This project details the release and characteristics of global large language models, providing a valuable resource for both open-source and closed-source LLMs developed after ChatGPT. It gathers essential data such as model sizes, supported languages, domains, and training datasets, alongside links to GitHub repositories, HuggingFace models, and academic publications. Regular updates keep users informed, with an invitation for contributions to enhance this dataset. Ideal for researchers and developers interested in the dynamics of natural language processing models.

Jcseg provides a comprehensive solution for Chinese text segmentation, based on the efficient mmseg algorithm and offering seven distinct modes. It integrates TextRank for extracting keywords, keyphrases, and key sentences and utilizes BM25 for automatic text summarization. The high-performance server module enables RESTful API access and seamless HTTP integration across different languages. Users can customize word libraries, use Simplified/Traditional Chinese word lists, and add synonyms and pinyin. The latest interfaces for Lucene, Solr, Elasticsearch, and OpenSearch are supported, with flexible configuration through jcseg.properties.

Explore the functionality of automated sentence generation through keywords to improve marketing, SEO, and topic development. This project utilizes the T5 model and provides extensive resources including tutorials, API access, and a user-friendly interface made with Streamlit. Enhance content strategies efficiently with cutting-edge natural language processing solutions.

A detailed repository of medical NLP resources including evaluations, competitions, datasets, papers, and pre-trained models, maintained by third-party contributors. It features Chinese and English benchmarks like CMB, CMExam, and PromptCBLUE, highlights ongoing and past events such as BioNLP Workshop and MedVidQA, and catalogs diverse datasets like Huatuo-26M and MedMentions. The repository also provides access to open-source models like BioBERT and BlueBERT, and large language models including ApolloMoE, catering to researchers in the medical NLP sphere.

The tool provides efficient solutions for augmenting Chinese text data, with features like random entity replacement and synonym swaps to improve NLP model generalization and resilience. It includes advanced functionality such as NER data augmentation and character replacement to maintain semantic integrity. Easy to install via pip, this tool helps generate extensive datasets while preserving the text's original meaning, enhancing model capacity and stability.

Ecco is a Python library designed for exploring and explaining Transformer models through interactive visualizations. It focuses on pre-trained models such as GPT2 and BERT, providing features like feature attribution, neuron activation capture, and activation space comparison within Jupyter notebooks. Built on PyTorch and Hugging Face's transformers, it helps visualize token predictions and neuron activation patterns, offering insights into the functions of NLP models.

Examine the memory footprint, parameter count, and FLOPs of state-of-the-art AI models in computer vision, NLP, and speech. Access detailed metrics for transformer and vision model training and inference, with historical memory breakdowns. This resource offers valuable data from the AI and Memory Wall study, aiding in optimizing model efficiency for contemporary applications.

Explore an intuitive Python toolkit that facilitates quick text data manipulation and visualization. This open-source solution integrates smoothly with Pandas for seamless preprocessing, vector representation, and visualization of text datasets. Incorporating TF-IDF, natural language processing, and clustering, it meets the demands of today's programmers with limited linguistic knowledge. Improve text analysis projects efficiently while supporting a growing multilingual community.

Distilabel is a framework for creating synthetic data and obtaining AI feedback, serving those developing NLP and LLM projects. It facilitates the creation of high-quality, varied datasets using established research techniques. The framework allows engineers to concentrate on enhancing data quality and controlling model tuning, integrating feedback across LLM providers with a single API. As an open-source, community-supported project, Distilabel ensures scalable and adaptable data generation pipelines to enhance the efficiency and quality of AI development.

Explore a detailed guide to PyTorch with tutorials, video lessons, and suggested readings. Discover practical applications in NLP and computer vision using a variety of PyTorch repositories. This resource caters to learners of all levels, providing comprehensive support from foundational neural network concepts to advanced model training techniques.

OpenCompass is a comprehensive platform for assessing large language models, featuring advanced algorithms and a user-friendly interface. It supports 20+ HuggingFace and API models, evaluating over 70 datasets with about 400,000 questions. The platform is proficient in distributed evaluations, providing billion-scale assessments within hours, and supports various paradigms including zero-shot and few-shot learning. OpenCompass is modular and easily extendable, accommodating new models and datasets. It also allows for API and accelerated evaluations with different backends, contributing to a fair, open, and reproducible benchmarking ecosystem with its tools like CompassKit, CompassHub, and CompassRank.

Discover an open-source framework for prompt-learning that enhances pre-trained language models to adapt to diverse NLP tasks through textual templates and PLMs. Key features include seamless integration with Huggingface transformers and flexible adaptable strategies for various applications. Stay informed about the latest project updates like UltraChat for supervised instruction tuning. OpenPrompt offers a standardized platform for simplified and efficient NLP model deployment.

This objective survey examines the intersection of NLP and software engineering via language models for code. It presents a chronological categorization of research papers, providing insights into basic language models, their adaptations for code, and pretraining methods. Key topics covered include reinforcement learning on code, analysis of AI-generated code, low-resource languages, and practical tasks such as code translation and program repair. Additionally, the survey includes recommended readings for those new to NLP, and updates on notable papers, serving as a valuable resource for understanding developments and uses of large language models in code-related fields.

Dodrio assists NLP researchers in analyzing transformer model attention weights with a focus on linguistic context. It provides an interactive demo, comprehensive setup instructions, and is acknowledged in leading academic discussions, facilitating a deeper understanding of model behavior.

Explore a diverse array of open-source datasets designed to improve chat-focused Large Language Models (LLMs) including ChatGPT, LLaMA, and Alpaca. This collection offers comprehensive datasets that support Instruction Tuning and Reinforcement Learning from Human Feedback (RLHF), crucial for developing instruction-following LLMs. Ideal for researchers and developers, it provides access to datasets spanning various languages and tasks, utilizing techniques such as human data generation, self-instruct, and mixed methodologies. This resource expedites advancements in natural language processing, fostering innovation.

Discover a broad spectrum of SDKs offering capabilities like image recognition, text analysis, and facial detection. With tools supporting multilingual OCR, face feature extraction, and image enhancement, these SDKs cater to diverse application needs. Understand the precision in text detection, cross-modal retrieval, and specialized classifiers for animals and dishes, facilitating effective AI solutions across sectors. Keep informed with our platform to effortlessly integrate state-of-the-art AI models tailored to specific requirements.

This resource offers carefully curated study notes and materials for natural language processing (NLP) interview preparation. It covers a broad array of interview questions across various NLP domains and provides thorough insights into algorithms such as Hidden Markov Model (HMM), Maximum Entropy Markov Model (MEMM), and Conditional Random Fields (CRF). Designed to support both novices and experienced professionals, the project addresses crucial topics like named entity recognition, relationship extraction, event extraction, and pre-training methods like TF-IDF and Word2Vec. Each section presents typical interview questions, explanations, and solutions, forming a comprehensive reference for NLP enthusiasts preparing for technical interviews.

The SimilaritySearchKit Swift package enables on-device text embeddings and semantic search suited for iOS and macOS applications. Prioritizing speed, extensibility, and privacy, it integrates numerous advanced NLP models and similarity metrics. Ideal for building privacy-focused search engines and offline QA systems, it is easily installed via the Swift Package Manager, allowing seamless integration while ensuring data privacy and efficient performance.

Access a range of spaCy NLP models for various language processing tasks, available as `.whl` and `.tar.gz` files for efficient downloads. Installation commands ensure compatibility across spaCy versions. Models are classified by capabilities, training data types, and sizes, offering flexibility for different applications. Consult the documentation for detailed installation guidance and usage instructions. This repository is suitable for developers looking for customizable NLP solutions.

The NLPer-Arsenal project offers an extensive compilation of strategies for NLP competitions, along with task tutorials, experience insights, and educational resources. It features plugin-based strategy testing, decoupled implementation methods, and insights into both seasonal and ongoing competitions. Additionally, the project provides recommendations for media platforms, compute resources, and tracks key NLP conference schedules. This curated and regularly updated collection is an essential tool for professionals seeking to advance their NLP capabilities or keep apprised of current trends and events.

Deepdoctection is an open-source Python library that facilitates document extraction and layout analysis through the integration of leading deep learning technologies. It supports the creation of flexible pipelines that utilize popular libraries for object detection, OCR, and natural language processing. Compatible with both Tensorflow and PyTorch, it provides extensive features for tasks like language detection, image deskewing, and table recognition. Analyze and process documents efficiently with customizable outputs and explore a wide range of tutorials and pre-trained models suitable for various industry applications.

This repository offers tools and examples for developing NLP systems using cutting-edge AI techniques. It features Jupyter notebooks and utility functions for state-of-the-art scenarios, supporting multilingual tasks such as text classification and intelligent chatbots. This resource highlights the use of pretrained models like BERT and transformers to speed up solution development, including integration with Azure Machine Learning and the use of prebuilt APIs for effective NLP task management.

This repository offers a curated selection of resources and study notes on AI, ML, and DL, aimed at engineers, developers, and data scientists. It provides easy access to key materials in areas like natural language processing and neural networks. It supports community contributions and regular updates enhance engagement. Explore practical guides, tools, and libraries designed to expand understanding of AI and ML.

AllenNLP is an Apache 2.0 licensed NLP research library built on PyTorch, designed for developing cutting-edge deep learning models across various linguistic tasks. Currently in maintenance mode, it has significantly contributed to the NLP field, simplifying experimentation with its template repositories and extending functionality through plugins. The library supports comprehensive model training, evaluation, and prediction. Users looking for similar capabilities can consider alternatives such as AI2 Tango, allennlp-light, flair, and torchmetrics. Contributions from those interested in maintaining the library are welcomed.

Explore Chinese NLP fundamentals through detailed explanations of key techniques including segmentation, POS tagging, and named entity recognition. Authored by HanLP creator Han He, this project translates complex models into digestible concepts, offering professional development opportunities with insightful personal notes. Access additional resources like mind maps and related project links for a comprehensive learning journey.

This project leverages the Mixtral-8x7B model, enhanced with an expanded Chinese vocabulary to improve NLP capabilities. It offers open-source access to both the expanded model and incremental pre-training code, which notably boosts encoding and decoding efficiency in Chinese, ensuring strong comprehension and generation potentials. Users should remain attentive to potential biases or inaccuracies in outputs. The model is compatible with various acceleration techniques within the Mixtral-8x7B ecosystem and can be downloaded including options for integrating LoRA weights.

Flair is a state-of-the-art natural language processing library offering tools for tasks like named entity recognition, sentiment analysis, and part-of-speech tagging. It is developed by Humboldt University of Berlin and supports a wide range of languages, with a particular focus on biomedical text processing. The library simplifies the use and combination of different embeddings with user-friendly interfaces and is built on PyTorch, which allows easy training of custom models. Comprehensive tutorials enable users to efficiently explore and deploy high-performance NLP models, with accessibility via platforms such as Hugging Face.

This repository focuses on Event Extraction in Natural Language Processing, covering methods from pattern matching to unsupervised learning. It includes resources like AutoSlog, LIEP, and REES, which enhance semantic lexicons with bootstrapping and pattern recognition techniques. The repository supports tasks in various domains, such as terrorism and stock market analysis, offering effective information extraction solutions.

Cherche supports creating neural search pipelines that utilize retrievers and pre-trained language models for effective document retrieval and ranking. It excels in constructing end-to-end solutions, suitable for offline semantic searches with batch processing. Cherche integrates with popular retrievers like TfIdf, Flash, Lunr, and uses SentenceTransformers for advanced ranking. Comprehensive documentation and live demos are available to assist users. It is MIT licensed and open for community contributions.

Discover NucliaDB, a comprehensive database built for efficient handling of unstructured data with a hybrid approach combining vector, full text, and graph indexing. Benefit from integration with Nuclia Cloud, offering advanced NLP capabilities without the need for complex data handling. NucliaDB supports multilingual environments, role-based security, and integrates seamlessly with popular NLP pipelines, covering a wide range of search requirements.

Refinery offers data-focused tools for improving NLP models, with features like semi-automated labeling, comprehensive data management, and monitoring. It treats training data as software artifacts to optimize their use and quality. Integrations with Hugging Face and spaCy streamline workflows, simplifying superior NLP model development.

Adapters enrich HuggingFace's Transformers by integrating over 10 adapter methods into 20+ models, supporting efficient fine-tuning and transfer learning. Key features include full-precision and quantized training, adapter task arithmetics, and multi-adapter compositions, facilitating advanced research in NLP. Compatible with Python 3.8+ and PyTorch 1.10+, it's an essential tool for optimizing models with ease of implementation.

Streamline Analyst utilizes AI to automate tasks such as data cleaning and model selection, offering efficient and accessible data analysis workflows. It includes features like results visualization, PCA, and balanced modeling with SMOTE and ADASYN. The tool supports classification, clustering, and regression tasks, and maintains data privacy. Future updates will introduce NLP and neural networks, expanding its analytical capabilities.

Access comprehensive lecture notes and resources from leading courses like Andrew Ng's Machine Learning and MIT's Deep Learning. Ideal for AI enthusiasts seeking insights into advanced and foundational AI topics.

PatrickStar uses chunk-based memory management to optimize CPU and GPU resources, enabling the training of large models with fewer GPUs. This makes PTM training more accessible. Compatible with PyTorch, it supports cost-effective scaling and outperforms solutions like DeepSpeed by managing up to 175 billion parameters on small clusters.

Explore a curated collection of large language model concepts and interview questions, particularly suited for resource-constrained scenarios. Discover 'tiny-llm-zh', a compact Chinese language model, alongside projects including llama and RAG systems for practical AI learning. Engage with resources on deep learning, machine learning, and recommendation systems.

Discover a vast array of PyTorch libraries and tutorials focused on NLP, CV, and probabilistic models. This curation serves researchers and developers with tools for neural networks, paper implementations, and improving model interpretability, utilizing PyTorch's GPU support and extensive library resources.

PostgresML integrates machine learning within PostgreSQL databases, combining GPU acceleration and support for Hugging Face's large language models. It features an efficient RAG pipeline for enhanced performance and security without data transfers. With compatibility for over 47 ML algorithms and efficient vector search using pgvector, PostgresML offers up to 40X faster inference than conventional methods, making it suitable for scalable AI applications with improved data privacy.

The project leverages the Hugging Face Transformers library to support a range of NLP tasks, facilitating seamless loading and training of transformer models. It enables easy dataset interchange for task-specific model training across domains such as text matching, information extraction, prompt engineering, and more. Detailed guidance and tool integration, including a tokenizer viewer, are provided. This resource supports diverse learning methods, enhancing NLP model customization without excessive promotional language.

ArticutAPI provides a syntactically driven method for Chinese word segmentation, ensuring precise and reliable outcomes as opposed to statistical approaches. Its design focuses on simplicity and flexibility, offering batch and real-time processing ideal for text analysis and chatbot use cases. Features like customizable semantic tools, user-defined lexicons, TF-IDF and TextRank for keyword extraction, and integration with open data enhance both linguistic processing and data analysis.

Convert text to graphics using AI technology. SolidUI offers 2D and 3D graphic models and scenes by merging natural language processing with computer graphics. Its unique Vincent graph language model benefits from reinforcement learning for improved accuracy. The platform supports containerized deployment, various data sources, Huggingface collaboration, and plug-in robotics for enhanced visualization tool development.

Delve into a comprehensive list of impactful publications on textual adversarial attack and defense. This resource includes surveys and methods for attacks and defenses, along with benchmark evaluations and certified robustness analyses. Authored by leading academics, it offers valuable insights for advancing research and application in adversarial NLP across various perturbation levels. Keep abreast of the latest innovations and methodologies in text attack and defense strategies.

Explore an extensive collection of educational resources in machine learning, deep learning, NLP, and TensorFlow. Featuring courses from leading institutions like Stanford and Google, along with comprehensive textbooks and research papers, these resources are perfect for expanding your knowledge and skills in the field.

This directory provides a well-organized collection of important natural language processing (NLP) research papers, including significant topics like Transformer frameworks, BERT variations, transfer learning, text summarization, sentiment analysis, question answering, and machine translation. It features notable works such as 'Attention Is All You Need' and detailed investigations into BERT's functions. Covering downstream tasks like QA and dialogue systems, interpretable machine learning, and specialized applications, this collection is a valuable resource for researchers and developers exploring advancements and techniques influencing current NLP practices, with a focus on practical implications in machine learning.

Discover how Transformers4Rec bridges NLP and recommender systems, providing a flexible and efficient solution for sequential and session-based recommendations. Integrated with Hugging Face Transformers and PyTorch, it supports over 64 transformer architectures and various input features. Utilize its seamless preprocessing and GPU-accelerated pipelines as part of the Merlin ecosystem to improve recommendation accuracy. A proven tool for researchers and industry professionals.