Models, data loaders and abstractions for language processing, powered by PyTorch
-
Updated
Feb 9, 2025 - 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.
French-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 + Flow Visual Git Diff
Python library for handling audio datasets.
A better way to work on Salesforce
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.
PyTorch DataLoader processed in multiple remote computation machines for heavy data processings
A higher order component for declarative data loading in React and Redux.
PyTorch library for Active Fine-Tuning
Benzina is an image-loader package that greatly accelerates image loading onto GPUs using their built-in hardware codecs.
PyTorch tutorial for computer vision
A tensorflow-based implementation of SISR using EDSR, SRResNet, and SRGAN
Cross service dependencies for GraphQL API with underlying @imqueue services
Training framework & tools for PyTorch-based machine learning projects.
Speed up the loading of your application by loading its required data in parallel to your app code!
Library containing some helper methods for GraphQL apis. Includes data-loaders and (cursor based) pagination helper methods.
A Python package to load complex XML files into a relational database
A utility for wrapping the Free Spoken Digit Dataset into PyTorch-ready data set splits.
Add a description, image, and links to the data-loader topic page so that developers can more easily learn about it.
To associate your repository with the data-loader topic, visit your repo's landing page and select "manage topics."