Models, data loaders and abstractions for language processing, powered by PyTorch
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Updated
Aug 26, 2024 - Python
Models, data loaders and abstractions for language processing, powered by PyTorch
Basic Utilities for PyTorch Natural Language Processing (NLP)
SFDMU is a cutting-edge Salesforce data migration tool for seamless org population from other orgs or CSV files. It handles all CRUD operations on multiple related objects in one go.
Swiss-army-knife Toolbox for Salesforce. Orchestrates base commands and assist users with interactive wizards to make much more than native sfdx + Allows you to define a complete CI/CD Pipeline and Schedule a daily Metadata backup & monitoring of your orgs
Python library for handling audio datasets.
A better way to work on Salesforce
PyTorch DataLoader processed in multiple remote computation machines for heavy data processings
DaLI (Data Loader Interface) is a data loader and input generator for RP2 (https://pypi.org/project/rp2), the privacy-focused, free, open-source cryptocurrency tax calculator: DaLI removes the need to manually prepare RP2 input files. Just like RP2, DaLI is also free, open-source and it prioritizes user privacy.
A higher order component for declarative data loading in React and Redux.
Benzina is an image-loader package that greatly accelerates image loading onto GPUs using their built-in hardware codecs.
PyTorch tutorial for computer vision
Cross service dependencies for GraphQL API with underlying @imqueue services
A tensorflow-based implementation of SISR using EDSR, SRResNet, and SRGAN
Training framework & tools for PyTorch-based machine learning projects.
Library containing some helper methods for GraphQL apis. Includes data-loaders and (cursor based) pagination helper methods.
Speed up the loading of your application by loading its required data in parallel to your app code!
A utility for wrapping the Free Spoken Digit Dataset into PyTorch-ready data set splits.
Apply infinite scroll to your website or web-app.
Implementation for Video Frame Interpolation Via Down-Up Scale Generative Adversarial Networks (2022).
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