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Published directory entries matching your search.
Facets is a project containing two visualizations, Facets Overview and Facets Dive, for understanding and analyzing machine learning datasets. The visualizations are Polymer web components backed by TypeScript that can be embedded in Jupyter notebooks or webpages. Overview produces feature-by-feature statistical analysis and compares statistics across datasets. The repository was archived on Jul 27, 2024 and is now read-only.
Provides a central interface to connect your LLM's with external data.
Turn entire websites into LLM-ready markdown or structured data. Scrape, crawl and extract with a single API.
ParadeDB is an open-source PostgreSQL extension hosted on GitHub. It adds functionality for application data management, full-text search, vector retrieval and data aggregations to PostgreSQL databases, and is the home of the pg_search extension. The repository includes links to associated documentation, community resources, a blog and a changelog.
Ambrosia is a cross-platform command line tool written in Go for improving text datasets used in machine learning. It offers fast deduplication, filtering, length checking and whitespace trimming, plus an LLM-based filtering option called psort. It ships as a single binary with no dependencies and is pre-1.0 software.
Awadb is an AI-native database built for working with embedding vectors. It is available as a public open-source repository on GitHub, where users can access the code, contribute to development, or deploy it locally on Mac OSX or Linux, or run it as a standalone service. The project is designed to support AI applications that rely on vector embedding functionality.
LinkedIn's generalized metadata search & discovery tool.
ImageBind is a PyTorch implementation with pretrained models from FAIR, Meta AI. It learns a joint embedding across six modalities: images, text, audio, depth, thermal, and IMU data. The release includes code and model weights under the CC-BY-NC 4.0 license.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
PandasAI is an open-source library that enables conversational data analysis using large language models and retrieval-augmented generation. It allows users to interact with databases or datalakes supporting SQL, CSV, and parquet file formats.
Labelbuddy is an open-source desktop GUI application for annotating documents, developed on GitHub. According to its documentation, it can be used for tasks such as Named Entity Recognition, sentiment analysis and document classification. The documentation states that it works completely offline and that its database is an ordinary file. Import and export use JSON, JSON Lines or plain text formats.
Lambdo is a workflow engine that unifies feature engineering and machine learning operations. In its workflows, nodes can be treated as features or predictions, and both can be trained. It uses column-oriented data processing and provides functions for time-series analysis and forecasting.
LlamaIndex is an open-source document processing platform built for artificial intelligence applications. It is maintained in a GitHub repository by run-llama, offering tools to handle documents as part of AI workflows.
mlxtend is a library that provides extension and helper modules for Python's data analysis and machine learning libraries. The project includes installation guides, example code, license information, and contact details. It is hosted as an open source repository on GitHub.
Instagram OSINT tool for collecting profile data, followers, posts, comments, and account signals.
Pattern is a web mining module for Python with tools for data mining, natural language processing, machine learning, and network analysis and visualization. The repository is archived and no longer maintained, receiving no further updates, bug fixes or security patches.
pprof is a tool for visualization and analysis of profiling data
PyBroker is a Python framework for developing algorithmic trading strategies with a focus on machine learning. It provides a backtesting engine built in NumPy and accelerated with Numba, model training and backtesting using Walkforward Analysis, parameter optimization with Optuna, and access to historical data from Alpaca, Yahoo Finance, and AKShare. It supports Python 3.11+ on Windows, Mac, and Linux.
Toolbox of models, callbacks, and datasets for AI/ML researchers.
AI-generated visualization prototyping and editing platform, support 2D, 3D models, combined with LLM(Large Language Model) for quick editing.
TextCL is a text preprocessing package intended for cleaning text data before use in natural language processing tasks. It can be an initial step in text analysis and in predictive, classification, or text generation models. Documented preprocessing tasks include splitting texts into sentences, filtering by language, perplexity filtering, duplicate sentence filtering using Jaccard similarity, and unsupervised outlier detection with methods such as TONMF, RPCA, and SVD.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
Vearch is a cloud-native distributed vector database for similarity search of embedding vectors in AI applications. It supports hybrid vector search and scalar filtering, replication, elastic scaling, RESTful APIs, and SDKs for Python, Go, Java and Rust. It integrates with frameworks including LangChain and LlamaIndex and can be deployed via Kubernetes or Docker Compose.
