A Modern C++ Data Sciences Toolkit
The main features of meta-toolkit/meta are: Machine Learning, Natural Language Processing, C++ NLP Libraries.
Open-source alternatives to meta-toolkit/meta include: johnsnowlabs/spark-nlp — Spark NLP is a toolkit for scalable text analysis and machine learning built on the Apache Spark distributed computing… huggingface/transformers — Transformers is a comprehensive library for machine learning that provides a unified interface for training,… bllip/bllip-parser — BLLIP reranking parser (also known as Charniak-Johnson parser, Charniak parser, Brown reranking parser) See… facebookresearch/starspace — Starspace is a vector embedding framework designed for training high-dimensional representations of text and images.… google-research/google-research — This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum… languagemachines/frog — Frog is an integration of memory-based natural language processing (NLP) modules developed for Dutch. All NLP modules…
This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed
Starspace is a vector embedding framework designed for training high-dimensional representations of text and images. It functions as a machine learning system for neural ranking, text classification, and knowledge graph embedding, mapping different object types into a shared numerical space to facilitate retrieval and prediction tasks. The system includes specialized tools for knowledge graph completion and link prediction by representing entities and their relationships within a multi-relational vector space. It further provides capabilities for semantic content recommendation and large-scal
BLLIP reranking parser (also known as Charniak-Johnson parser, Charniak parser, Brown reranking parser) See http://pypi.python.org/pypi/bllipparser/ for Python module.
Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering specialized architectures for both text and vision processing. The framework includes tools for managing the entire model lifecycle, from data preprocessing and tokenization to distributed training and inference. The library features extensive support for model optimization and