Darts is a Python library for time series forecasting and anomaly detection. It provides a unified API for a wide range of models, from classical statistical methods (ARIMA, Exponential Smoothing) to deep learning architectures (N-BEATS, TFT, TiDE, Transformer) and foundation models (Chronos, TimesFM, PatchTST-FM). The library also includes data preprocessing transformers (scaling, missing value filling, Box-Cox, DTW), anomaly detection scorers and detectors, explainability tools (SHAP, TFT attention), and comprehensive evaluation metrics. The architecture is modular: core TimeSeries data structure, model registry with lazy imports, transformer pipeline, and safe serialization. External dependencies include pandas, numpy, scikit-learn, PyTorch, PyTorch Lightning, and many optional packages for specific models. The codebase is well-tested with extensive unit and integration tests, and follows good engineering practices with CI/CD, dependency audits, and semantic versioning.
This repository provides the infrastructure for the Anticipatory Music Transformer, a research project for music generation using autoregressive transformers with anticipatory infilling. The codebase covers the full pipeline: preprocessing of MIDI files (Lakh MIDI dataset) into a compound token representation, tokenization into arrival-time or interarrival-time sequences with optional augmentation for anticipation training, and sampling from trained models using custom generation logic. It also includes scripts for human evaluation reproduction and model benchmarking. While primarily a research codebase for a specific paper, the library components in the `anticipation` package (particularly the conversion, ops, and sampling modules) are well-structured and could be reused in other music tokenization or generation projects. The repository does not include the training loop itself but provides configuration files for use with the Levanter training framework.
This repository provides a Python library for converting web pages into text optimized for Large Language Model (LLM) input. It fetches HTML content using Playwright (headless browser) and processes it to extract meaningful text while preserving structural information like tables, links, and images in a text format. The core processing logic converts HTML tables to markdown, handles image and link preservation (or removal), and strips unnecessary tags (script, style) to reduce noise. The library is designed as a preprocessing step before feeding web content to LLMs for tasks such as data extraction or summarization. It includes three main modules: HTML fetching, HTML processing, and a combined convenience function.
Visual Spatial Tuning (VST) is a research framework for enhancing Vision-Language Models (VLMs) with human-like visuospatial abilities, from perception to reasoning. It implements a two-stage pipeline: supervised fine-tuning (SFT) on 4.1M spatial perception samples (VST-P) followed by reinforcement learning (RL) on 135K reasoning samples (VST-R). The framework extends to Vision-Language-Action (VLA) models for robotic manipulation evaluated on the LIBERO benchmark. The repository contains custom model implementations (Qwen2.5VL, Qwen3VL) with sequence parallelism and FSDP support, data preprocessing scripts, evaluation benchmarks (VSIBench, 3D object detection on SUN RGB-D, VLM benchmarks), and a distributed RL training system (verl) with reward functions and advantage estimation. While primarily a research codebase, it includes production-quality training infrastructure and reusable data utilities. The code is actively maintained with recent commits, but has medium technical drift due to tight version pinning and reliance on an internal ByteDance framework (VeOmni) that is not publicly available, which may hinder full reproducibility.
This repository provides a template for training YOLO models on custom datasets (primarily from Kaggle) and performing inference. It includes command-line scripts for training and inference, a Streamlit app for interactive use, a Jupyter notebook for step-by-step execution, and utility scripts for data preprocessing, dataset exploration, and auto-labeling. The system allows users to download datasets, preprocess them (cleaning and augmentation), train YOLO models of various sizes, run inference on images/videos/webcam, export models to NCNN format, and automatically label images using GroundingDINO. The code is structured into modular components, with notable reusable parts in the data preprocessing and dataset exploration modules.
TensorFlow Transform (tf.Transform) is a production-grade library for large-scale data preprocessing in TensorFlow pipelines. It computes global statistics (analyzers) over full datasets and applies them as transformations, ensuring train/serve consistency. The library integrates with Apache Beam for distributed execution and is part of the TFX ecosystem. Users define a preprocessing function using tft analyzers (e.g., min, max, mean, quantiles, vocabulary, TF-IDF, PCA) and mappers (e.g., scaling, bucketization, n-grams), which is traced and exported as a TensorFlow SavedModel. The architecture revolves around a graph of operation nodes (analyzer_nodes.py) that are traversed and translated into Beam PTransforms. Key features include incremental caching, combiner packing for performance, deep copy of Beam collections, and schema inference. The codebase is well-structured with extensive tests, but carries technical debt from TF1/TF2 migration, some known test failures (xfail), and experimental APIs. It is actively maintained within the TFX ecosystem.
This repository is primarily an educational resource for teaching data science and machine learning concepts. It contains a extensive collection of Jupyter notebooks, Python scripts, and supporting materials covering topics from basic statistics and pandas to advanced deep learning, NLP, and LLMs. The content is organized as tutorials and examples rather than a cohesive application, with numerous datasets included for hands-on learning. The repository appears to be actively maintained with recent updates, suggesting efforts to keep the educational content current with evolving technologies.
This repository is a companion to the Packt book 'Python Feature Engineering Cookbook' by Soledad Galli. It contains over 70 Jupyter notebooks organized by chapter, each demonstrating a specific feature engineering technique for machine learning, including missing data imputation, categorical encoding, variable transformation, discretization, outlier handling, date/time features, scaling, feature creation, and text feature extraction. The notebooks use public datasets (Titanic, Boston Housing, Credit Approval UCI, 20 Newsgroups) and implement techniques both manually with pandas and using specialized libraries like feature_engine, category_encoders, and featuretools. While educational and self-contained, the code is not production-grade and relies on outdated library versions and deprecated datasets (e.g., load_boston), making it a tutorial resource rather than a reusable software project. The repository is stale—last updated around 2020—with no ongoing maintenance, and missing dependencies (e.g., category_encoders not listed in requirements.txt) further reduce reproducibility.