Search Results for "text analysis" - Page 2
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Showing 143 open source projects for "text analysis"
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Qwen2-Audio
Repo of Qwen2-Audio chat & pretrained large audio language model
Qwen2-Audio is a large audio-language model by Alibaba Cloud, part of the Qwen series. It is trained to accept various audio signal inputs (including speech, sounds, etc.) and perform both voice chat and audio analysis, producing textual responses. It supports two major modes: Voice Chat (interactive voice only input) and Audio Analysis (audio + text instructions), with both base and instruction-tuned models. It is evaluated on many benchmarks (speech recognition, translation, sound classification, emotion, etc.), and offers pretrained models (e.g. 7B) released via ModelScope and Hugging Face. ... -
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Introduction-NLP
Detailed notes on HanLP's new book, "Introduction to Natural Language"
Introduction NLP is a Chinese-language study-notes repository based on the book Introduction to Natural Language Processing by the author of HanLP. It explains NLP concepts in accessible language while pairing theory with practical implementation notes. The material begins with basic concepts and Chinese word segmentation before progressing into statistical sequence models. Later chapters cover part-of-speech tagging, named entity recognition, information extraction, text clustering, text... -
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flair
A very simple framework for state-of-the-art NLP
...Developed by Humboldt University of Berlin and friends. A powerful NLP library. Flair allows you to apply our state-of-the-art natural language processing (NLP) models to your text, such as named entity recognition (NER), sentiment analysis, part-of-speech tagging (PoS), special support for biomedical texts, sense disambiguation and classification, with support for a rapidly growing number of languages. A text embedding library. Flair has simple interfaces that allow you to use and combine different word and document embeddings, including our proposed Flair embeddings and various transformers. ... -
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Biomni
Biomni: a general-purpose biomedical AI agent
...It integrates retrieval-augmented generation with code-based execution, allowing it to access external knowledge, process data, and generate testable hypotheses in scientific workflows. The system is built to support researchers by automating repetitive and time-consuming tasks such as literature review, data analysis, and experimental design. Biomni operates within a comprehensive environment that includes tools, APIs, and datasets, enabling it to execute multi-step research processes rather than just generating text responses. It supports integration with multiple AI models, allowing flexibility in selecting the most appropriate model for specific tasks. -
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DataProfiler
Extract schema, statistics and entities from datasets
DataProfiler is an AI-powered tool for automatic data analysis and profiling, designed to detect patterns, anomalies, and schema inconsistencies in structured and unstructured datasets. The DataProfiler is a Python library designed to make data analysis, monitoring, and sensitive data detection easy. Loading Data with a single command, the library automatically formats & loads files into a DataFrame. Profiling the Data, the library identifies the schema, statistics, entities (PII / NPI), and... -
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DeerFlow
Deep Research framework, combining language models with tools
DeerFlow is an open-source, community-driven “deep research” framework / multi-agent orchestration platform developed by ByteDance. It aims to combine the reasoning power of large language models (LLMs) with automated tool-use — such as web search, web crawling, Python execution, and data processing — to enable complex, end-to-end research workflows. Instead of a monolithic AI assistant, DeerFlow defines multiple specialized agents (e.g. “planner,” “searcher,” “coder,” “report generator”)... -
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pangu.py
Paranoid text spacing in Python
...It’s designed to be pragmatic and lightweight, with sensible defaults that handle common edge cases found in websites, blogs, and multilingual technical docs. Because it targets clarity over heavy linguistic analysis, it’s easy to adopt and delivers immediate, visible improvements to mixed CJK/Latin text. -
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FlexLLMGen
Running large language models on a single GPU
FlexLLMGen is an open-source inference engine designed to run large language models efficiently on limited hardware resources such as a single GPU. The system focuses on high-throughput generation workloads where large batches of text must be processed quickly, such as large-scale data extraction or document analysis tasks. Instead of requiring expensive multi-GPU systems, the framework uses techniques such as memory offloading, compression, and optimized batching to run large models on commodity hardware. The architecture distributes computation and memory usage across the GPU, CPU, and disk in order to maximize the number of tokens processed during inference. ... -
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Former.skill
AI skill generator designed to reconstruct conversational personality
Ex Skill is an AI skill generator designed to reconstruct the conversational personality and shared memories of a former partner from personal data. It can analyze exported chats, photos, social media records, PDFs, images, and manually entered descriptions. The system builds separate memory and persona layers covering relationship history, habits, emotional patterns, communication style, and behavior. Generated personas can respond in a style modeled after the source material. New... -
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Text Length
Real-time word counter and reading time calculator
Text Length is a lightweight desktop utility designed for rapid text analysis. It operates entirely offline on your computer, ensuring complete privacy with zero data transmission to external servers. Featuring a clean dark minimalist interface, the app tracks characters (with/without spaces), words, and paragraphs in real time as you type. -
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NeMo Retriever Library
Document content and metadata extraction microservice
NeMo Retriever Library is a scalable microservice framework designed for extracting, structuring, and enriching content from documents to support downstream generative AI applications. It processes various document types by splitting them into components such as text, tables, charts, and images, and then applies OCR and contextual analysis to convert them into structured data formats. The system is built on NVIDIA NIM microservices, enabling high-performance parallel processing and efficient handling of large datasets. It supports multiple extraction strategies for different document formats, balancing accuracy and throughput depending on the use case. ... -
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Llama Cookbook
Solve end to end problems using Llama model family
The Llama Cookbook is the official Meta LLaMA guide for inference, fine‑tuning, RAG, and multi-step use-cases. It offers recipes, code samples, and integration examples across provider platforms (WhatsApp, SQL, long context workflows), enabling developers to quickly harness LLaMA models -
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MiniCPM-o
A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming
MiniCPM-o 2.6 is a cutting-edge multimodal large language model (MLLM) designed for high-performance tasks across vision, speech, and video. Capable of running on end-side devices such as smartphones and tablets, it provides powerful features like real-time speech conversation, video understanding, and multimodal live streaming. With 8 billion parameters, MiniCPM-o 2.6 surpasses its predecessors in versatility and efficiency, making it one of the most robust models available. It supports... -
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LOTUS
AI-Powered Data Processing: Use LOTUS to process all of your datasets
...The system provides a declarative programming model that allows developers to express complex AI data operations using high-level commands rather than manually orchestrating model calls. It offers a Python interface with a Pandas-like API, making it familiar for data scientists and engineers already working with data analysis libraries. The core concept of the framework is the use of semantic operators, which extend traditional relational database operations to support reasoning over text and other unstructured data. These operators allow tasks such as semantic filtering, ranking, clustering, and summarization to be expressed directly within data processing pipelines. ... -
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LongBench
LongBench v2 and LongBench (ACL 25'&24')
...Traditional language model benchmarks typically evaluate tasks involving relatively short inputs, which does not reflect many real-world applications such as analyzing large documents or entire code repositories. LongBench addresses this gap by providing datasets that require models to process and reason over long sequences of text across multiple tasks. The benchmark includes multiple categories such as single-document question answering, multi-document reasoning, summarization, long dialogue understanding, and code analysis. It supports bilingual evaluation in English and Chinese to assess multilingual capabilities across extended contexts. Newer versions of the benchmark introduce extremely long context windows ranging from thousands to millions of tokens, enabling researchers to test the limits of modern long-context models. -
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DocETL
A system for agentic LLM-powered data processing and ETL
...The platform allows developers and researchers to construct structured workflows that extract, transform, and organize information from sources such as reports, transcripts, legal documents, and other text-heavy data. Instead of relying on single prompts or ad-hoc scripts, DocETL provides a declarative pipeline framework that breaks complex document analysis tasks into manageable operations that can be optimized and orchestrated automatically. Pipelines are typically defined using a low-code YAML interface, giving users full control over prompts and processing steps while still simplifying workflow creation. -
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Janus
Unified Multimodal Understanding and Generation Models
...Rather than having separate systems for “look and describe” and “prompt and generate”, Janus uses an autoregressive transformer framework with a decoupled visual encoder—allowing it to ingest images for comprehension and to produce images from text prompts with shared internal representations. The design tackles long-standing conflicts in multimodal models: namely that the visual encoder has to serve both analysis (understanding) and synthesis (generation) roles. By splitting those pathways but keeping one unified core transformer, Janus maintains flexibility and achieves strong performance across tasks previously requiring distinct architectures. ... -
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Qwen-MM-Plugins
Make any agent harness multimodal-native
Qwen-MM-Plugins is a collection of native multimodal capabilities that can extend Qwen-powered agent harnesses beyond text-only workflows. Each capability combines an instructional skill with an optional MCP server that exposes specialized tools. The core package can inspect images, videos, documents, and 3D models while providing OCR, grounding, segmentation, transcription, and visual question answering. Additional modules support long-video memory, audio-video analysis, media generation, video editing, Blender, FreeCAD, and educational content creation. ... -
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Software Copyright Materials Skill
Skills, a Chinese software copyright application material generator
Software Copyright Skill is an open-source Codex skill for generating Chinese software copyright application materials from a local software project. It helps developers prepare the documents required for a software copyright filing without relying on paid document-preparation services. The skill reads the real project, guides the user through key confirmations, and produces organized materials that can be reviewed and edited locally. It can generate application-form reference information,... -
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SCAIL
Towards Studio-Grade Character Animation via In-Context Learning of 3D
...While specific documentation about SCAIL’s exact goals and implementation is limited from the repository context alone, the project appears to be part of a collection of machine learning and AI research tools that facilitate scalable model development, evaluation, or application workflows. Given its listing alongside other ZAI projects like speech recognition and text-to-speech systems, SCAIL likely emphasizes scalable, composable AI learning frameworks that support researchers and practitioners in experimenting with learning algorithms, datasets, and model components. The repository structure suggests a focus on flexibility and extensibility, with potential integration into other ZAI tooling for training or analysis. -
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Databend
Cloud-native open source data warehouse for analytics and AI queries
...This architecture enables cost-efficient storage and elastic scaling for workloads that involve large datasets and complex queries. Databend provides a unified engine capable of handling analytics, vector search, and full-text search within a single platform. Databend supports SQL-based workflows and enables real-time data ingestion, transformation, and analysis through streaming and task orchestration features. With its cloud-native design and distributed architecture, Databend can run both as a self-hosted system or within managed environments to power data analytics, AI workloads, and large-scale data. -
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Gitingest
Create prompt-friendly codebase digests from any Git repository URL
Gitingest is a developer utility that converts an entire Git repository into a structured, prompt-friendly text digest suitable for use with large language models. It analyzes a repository and produces a consolidated textual representation that includes the file structure and code content in an organized format. This makes it easier to provide meaningful code context when working with AI systems that require compact, readable inputs. Developers can generate these digests from either a local... -
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LangExtract
A Python library for extracting structured information
LangExtract is a Python library developed by Google that leverages large language models (LLMs) to extract structured information from unstructured text—such as clinical notes, research papers, or literary works—based on user-defined instructions. It is designed to transform free-form text into reliable, schema-constrained data while maintaining traceability back to the source material. Each extracted entity is precisely grounded in its original context, allowing visual inspection and... -
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spaCy models
Models for the spaCy Natural Language Processing (NLP) library
spaCy is designed to help you do real work, to build real products, or gather real insights. The library respects your time, and tries to avoid wasting it. It's easy to install, and its API is simple and productive. spaCy excels at large-scale information extraction tasks. It's written from the ground up in carefully memory-managed Cython. If your application needs to process entire web dumps, spaCy is the library you want to be using. Since its release in 2015, spaCy has become an industry... -
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GLM-4.6V
GLM-4.6V/4.5V/4.1V-Thinking, towards versatile multimodal reasoning
GLM-4.6V represents the latest generation of the GLM-V family and marks a major step forward in multimodal AI by combining advanced vision-language understanding with native “tool-call” capabilities, long-context reasoning, and strong generalization across domains. Unlike many vision-language models that treat images and text separately or require intermediate conversions, GLM-4.6V allows inputs such as images, screenshots or document pages directly as part of its reasoning pipeline — and...
