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Showing 5 open source projects for "python::module"
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AI-First Supply Chain ManagementLogility is a market-leading provider of AI-first supply chain management solutions engineered to help organizations build sustainable digital supply chains that improve people’s lives and the world we live in. The company’s approach is designed to reimagine supply chain planning by shifting away from traditional “what happened” processes to an AI-driven strategy that combines the power of humans and machines to predict and be ready for what’s coming. Logility’s fully integrated, end-to-end platform helps clients know faster, turn uncertainty into opportunity, and transform the supply chain from a cost center to an engine for growth.
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Teradata VantageCloud Enterprise is a data analytics platform for performing advanced analytics on AWS, Azure, and Google Cloud.VantageCloud is the complete cloud analytics and data platform, delivering harmonized data and Trusted AI for all. Built for performance, flexibility, and openness, VantageCloud enables organizations to unify diverse data sources, run complex analytics, and deploy AI models—all within a single, scalable platform.
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imodelsX
Interpretable prompting and models for NLP
Interpretable prompting and models for NLP (using large language models). Generates a prompt that explains patterns in data (Official) Explain the difference between two distributions. Find a natural-language prompt using input-gradients. Fit a better linear model using an LLM to extract embeddings. Fit better decision trees using an LLM to expand features. Finetune a single linear layer on top of LLM embeddings. Use these just a like a sci-kit-learn model. During training, they fit better... -
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solo-learn
Library of self-supervised methods for visual representation
A library of self-supervised methods for visual representation learning powered by Pytorch Lightning. A library of self-supervised methods for unsupervised visual representation learning powered by PyTorch Lightning. We aim at providing SOTA self-supervised methods in a comparable environment while, at the same time, implementing training tricks. The library is self-contained, but it is possible to use the models outside of solo-learn. -
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CTranslate2
Fast inference engine for Transformer models
CTranslate2 is a C++ and Python library for efficient inference with Transformer models. The project implements a custom runtime that applies many performance optimization techniques such as weights quantization, layers fusion, batch reordering, etc., to accelerate and reduce the memory usage of Transformer models on CPU and GPU. The execution is significantly faster and requires less resources than general-purpose deep learning frameworks on supported models and tasks thanks to many advanced optimizations: layer fusion, padding removal, batch reordering, in-place operations, caching mechanism, etc. ... -
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Hyperformer
Hypergraph Transformer for Skeleton-based Action Recognition
This is the official implementation of our paper "Hypergraph Transformer for Skeleton-based Action Recognition." Skeleton-based action recognition aims to recognize human actions given human joint coordinates with skeletal interconnections. By defining a graph with joints as vertices and their natural connections as edges, previous works successfully adopted Graph Convolutional networks (GCNs) to model joint co-occurrences and achieved superior performance. More recently, a limitation of... -
Pest Control Management SoftwareBuilt for the pest control industry, but also works great for Mosquito Control, Bin Cleaning, Window Washing, Solar Panel Cleaning, and other Home Service Businesses in need of an easy-to-use software that helps you simplify routing, scheduling, communications, payment processing, truck tracking, time tracking, and reporting.
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5
Neuro-comma
Punctuation restoration production-ready model for Russian language
...The Library doesn't use any high-level frameworks, such as PyTorch-lightning or Keras, to reduce the level entry threshold. Feel free to fork this repo and edit model or dataset classes for your purposes. Our team always uses the latest version and features of Python. We started with Python 3.9, but realized, that there is no FastAPI image for Python 3.9. There is several PRs in image repositories, but no response from maintainers. So we decided to change code which we use in production to work with the 3.8 version of Python. In some functions we have 3.9 code, but we still use them, these functions are needed only for development purposes.
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