Search Results for "spreadsheet machine learning" - Page 23
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Showing 1574 open source projects for "spreadsheet machine learning"
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AudioMuse-AI
AudioMuse-AI is an Open Source Dockerized environment
...AudioMuse-AI integrates with several popular self-hosted music servers including Jellyfin, Navidrome, and Emby, allowing users to extend existing media servers with advanced AI-powered recommendation capabilities. The system uses machine learning and audio analysis tools such as Librosa and ONNX models to extract features directly from audio tracks. -
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ModernBERT
Bringing BERT into modernity via both architecture changes and scaling
...ModernBERT introduces architectural improvements that enhance both training efficiency and inference performance, making the model more suitable for modern large-scale machine learning pipelines. The repository also includes FlexBERT, a modular framework that allows developers to experiment with different encoder building blocks and configurations when constructing new models. -
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AWS Toolkit for Visual Studio Code
Local Lambda debug, CodeWhisperer, SAM/CFN syntax, etc.
...The AWS CDK Explorer enables you to work with AWS Cloud Development Kit (CDK) applications. It shows a top-level view of your CDK applications that have been synthesized in your workspace. Amazon CodeWhisperer provides inline code suggestions using machine learning and natural language processing on the contents of your current file. Supported languages include Java, Python and Javascript. -
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React Native ExecuTorch
Declarative way to run AI models in React Native on device
...The project supports a range of AI use cases, including large language models, computer vision, OCR, object detection, speech processing, segmentation, and embeddings. It helps React Native developers use local AI capabilities without needing deep native programming or machine learning infrastructure expertise. The library is especially relevant for privacy-first apps, offline experiences, and mobile products that need low-latency inference. Its main value is bridging React Native with native AI execution so apps can run powerful models directly on user devices. -
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AI-Trader
100% Fully-Automated Agent-Native Trading
AI-Trader is an open-source AI-powered quantitative trading framework designed to combine financial analysis, machine learning, and autonomous trading workflows into a unified research platform. The project integrates large language models, financial indicators, market analysis pipelines, and automated decision-making systems to support strategy generation and market prediction tasks. It is built to help researchers and developers experiment with AI-assisted trading strategies using historical and real-time financial data. ... -
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Vector Admin
The universal tool suite for vector database management
Vector Admin is a full-stack management platform designed to simplify the handling, visualization, and control of vector databases used in modern AI and machine learning applications. It acts as a universal interface for interacting with multiple vector database providers such as Pinecone, Chroma, Weaviate, and Qdrant, enabling users to manage embeddings without relying solely on APIs or backend tooling. The platform provides a user-friendly graphical interface that allows developers and non-technical users alike to inspect, edit, and organize vectorized data in a way that is typically opaque in standard workflows. ... -
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Lingua-Py
The most accurate natural language detection library for Python
...Other use cases, for instance, might include routing e-mails to the right geographically located customer service department, based on the e-mails' languages. Language detection is often done as part of large machine learning frameworks or natural language processing applications. In cases where you don't need the full-fledged functionality of those systems or don't want to learn the ropes of those, a small flexible library comes in handy. -
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SDGym
Benchmarking synthetic data generation methods
...Or write your own custom machine learning model. In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. Install SDGym using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device. -
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Scientific Agent Skills
A set of ready to use Agent Skills for research, science, engineering
...It supports any AI agent compatible with the Agent Skills standard, including tools such as Cursor, Claude Code, Codex, and Gemini CLI. The repository includes 135 skills across scientific domains such as genomics, cheminformatics, clinical research, medical imaging, machine learning, physics, materials science, geospatial analysis, and scientific writing. Each skill provides curated documentation, examples, best practices, and integration guidance so agents can execute complex workflows more reliably. It is especially useful for researchers who need AI assistance with databases, Python libraries, literature review, data analysis, and scientific communication. ... -
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NOFX
Open source AI trading OS for autonomous multi-model trading systems
NOFX is an open source AI-powered trading operating system designed to automate financial trading workflows using autonomous AI agents. It acts as an infrastructure layer that transforms market data into AI-driven trade decisions and execution. Instead of requiring users to manually configure machine learning models, data sources, and API integrations, the system allows AI components to perceive market conditions, select models, and perform trading actions automatically. It supports running multiple AI models simultaneously and allows them to compete or collaborate when making trading decisions. NOFX integrates trading infrastructure such as exchange connectivity, strategy management, and performance monitoring into a single environment. ... -
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Gemma 4 Browser Assistant
On-device AI agent Chrome extension powered by Transformers.js
Gemma 4 Browser Assistant is an open-source browser extension that embeds an AI assistant directly into the browsing experience, powered by on-device machine learning models. It uses Transformers.js and Gemma models to run inference locally in the browser, eliminating the need for external servers and preserving user privacy. The extension includes a side panel interface that allows users to interact with the AI while browsing, enabling tasks such as summarizing pages and answering questions. ... -
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OpenAI Privacy Filter
Bidirectional token-classification model for identifiable info
OpenAI Privacy Filter is an open-weight machine learning model designed to detect and mask personally identifiable information in text with high efficiency and contextual awareness. It operates as a bidirectional token classification system that labels sensitive data in a single forward pass rather than generating text sequentially, enabling fast processing for large datasets. -
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Alibi Explain
Algorithms for explaining machine learning models
Alibi is a Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models. -
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ClawTeam
ClawTeam: Agent Swarm Intelligence (One Command → Full Automation)
...These agents communicate, share insights, and dynamically adapt their strategies based on real-time feedback, creating a form of collective intelligence. The framework supports a wide range of use cases, including software development, machine learning research, financial analysis, and content production. It is designed to work with various AI tools and command-line agents, making it highly flexible and extensible. ClawTeam also includes monitoring tools such as dashboards and tmux-based views to observe agent activity and progress. -
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Ultralytics
Ultralytics YOLO
Ultralytics is a comprehensive computer vision framework that provides state-of-the-art implementations of the YOLO (You Only Look Once) family of models, enabling developers to perform tasks such as object detection, segmentation, classification, tracking, and pose estimation within a unified system. It is designed to be fast, accurate, and easy to use, offering both command-line and Python-based interfaces for training, validation, and deployment of machine learning models. The framework supports a full end-to-end workflow, including dataset preparation, model training, evaluation, and export to various deployment formats. Its architecture emphasizes performance optimization, balancing speed and accuracy to support real-time applications across industries. Ultralytics also provides pretrained models and flexible configuration options, allowing users to adapt the system to different datasets and use cases with minimal effort. -
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Instill Core
Instill Core is a full-stack AI infrastructure tool for data
Instill Core is an open-source, full-stack AI infrastructure platform designed to orchestrate data pipelines, machine learning models, and unstructured data processing into a unified, production-ready system. It provides an end-to-end solution that enables developers to build, deploy, and manage AI-powered applications without needing to manually stitch together multiple tools across the data and model lifecycle. The platform focuses heavily on handling unstructured data such as documents, images, audio, and video, transforming them into AI-ready formats through integrated ETL pipelines and processing workflows. ... -
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MetaScreener
AI-powered tool for efficient abstract and PDF screening
...The system helps researchers analyze large collections of academic abstracts and research papers to determine which studies are relevant for inclusion in evidence synthesis projects. Instead of manually reviewing hundreds or thousands of documents, researchers can use MetaScreener to apply machine learning techniques that assist with classification and prioritization of candidate papers. The platform can analyze both abstracts and full PDF documents, enabling automated filtering based on research criteria defined by the user. By incorporating natural language processing techniques, the system can identify potentially relevant studies and reduce the workload associated with manual screening. -
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Lobe Icons
Brings AI/LLM brand logos to your React & React Native apps
...The library includes icons for a wide range of AI providers and models, allowing developers to visually represent integrations with tools such as large language models, AI APIs, and machine learning platforms. These icons are distributed in multiple formats including SVG, PNG, and WebP so they can be used in both web and mobile applications. -
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LLM-Finetuning
LLM Finetuning with peft
LLM-Finetuning is an open educational repository that provides practical notebooks and tutorials for fine-tuning large language models using modern machine learning frameworks. The project focuses on parameter-efficient fine-tuning methods such as LoRA and QLoRA, which allow large models to be adapted to new tasks without requiring full retraining. Instead of requiring specialized hardware or complex training pipelines, many examples are designed to run in cloud notebook environments such as Google Colab. ... -
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WeClone
One-stop solution for creating your digital avatar from chat history
...Developers can use the resulting model to create chatbots that simulate a specific user’s communication patterns for testing or research purposes. Overall, WeClone explores the idea of digital identity replication through machine learning and conversational modeling. -
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Z80-μLM
Z80-μLM is a 2-bit quantized language model
...A key deliverable is producing CP/M-compatible .COM binaries, enabling a genuinely vintage “chat with your computer” experience on real hardware or accurate emulators. The project sits at the intersection of machine learning and systems constraints, showing how model architecture, quantization, and inference code generation can be adapted to extreme memory and compute limits. It also functions as an educational reference for how to reduce inference to operations that fit an old-school instruction set and runtime environment. -
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Superduper
Superduper: Integrate AI models and machine learning workflows
Superduper is a Python-based framework for building end-2-end AI-data workflows and applications on your own data, integrating with major databases. It supports the latest technologies and techniques, including LLMs, vector-search, RAG, and multimodality as well as classical AI and ML paradigms. Developers may leverage Superduper by building compositional and declarative objects that out-source the details of deployment, orchestration versioning, and more to the Superduper engine. This... -
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Mosec
A high-performance ML model serving framework, offers dynamic batching
Mosec is a high-performance and flexible model-serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. -
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ToolUniverse
Democratizing AI scientists with ToolUniverse
...It standardizes how AI systems discover, select, and execute tools by introducing a unified AI-Tool Interaction Protocol that allows models to seamlessly connect with hundreds of scientific resources, including machine learning models, datasets, APIs, and analytical packages. Instead of requiring custom pipelines or fine-tuning, ToolUniverse wraps around existing models and enables them to reason, experiment, and iterate on complex workflows such as drug discovery, data analysis, and hypothesis testing. The platform abstracts tool usage behind a consistent interface, allowing AI agents to compose multi-step workflows, refine tool definitions automatically, and even generate new tools from natural language descriptions. -
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Hugging Face - Speech To Speech
Open speech-to-speech models and pipelines by Hugging Face toolkit AI
This project from Hugging Face focuses on enabling direct speech-to-speech processing using modern machine learning models. It provides tools and reference implementations that allow audio input to be transformed into audio output without requiring an intermediate text representation. Hugging Face - Speech To Speech builds on recent advances in speech modeling, combining components such as speech recognition, translation, and synthesis into unified pipelines.
