How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
The main features of code-kern-ai/refinery are: Text Annotation Tools, Data Annotation and Synthesis.
Projects with overlapping indexed features include: doccano/doccano — Doccano is a collaborative data labeling platform and machine learning dataset management system. It provides a… chakki-works/doccano — Doccano is a collaborative labeling platform and text annotation tool designed to create training data for machine… batsresearch/nplm — Welcome to NPLM (Noisy Partial Label Model), a programmatic weak supervision system that supports (partial) labeling… cleanlab/cleanlab — Cleanlab is a data-centric AI library and toolkit designed to improve machine learning model performance by detecting… cogcomp/talen — -->. autonlab/weasel — This is a PyTorch-Lightning-based framework, based on our End-to-End Weak Supervision paper (NeurIPS 2021), that…
Doccano is a collaborative data labeling platform and machine learning dataset management system. It provides a web-based interface for teams to import raw text, mark datasets, and export structured annotations for model training. The project specifically supports text annotation for classification and named entity recognition tasks. It enables teams to coordinate multiple users on a single project to maintain consistent labeling guidelines and increase the speed of dataset creation. The system includes tools for data management and team coordination, providing the ability to import raw data
Doccano is a collaborative labeling platform and text annotation tool designed to create training data for machine learning. It provides a specialized interface for performing sequence labeling and text classification on natural language datasets. The system functions as a supervised learning dataset manager, allowing multiple users to coordinate within a shared workspace to label datasets for natural language processing tasks. It supports the preparation of raw text data for model training by converting unstructured documents into structured labeled examples. The platform includes capabilit
Welcome to NPLM (Noisy Partial Label Model), a programmatic weak supervision system that supports (partial) labeling functions with supervision granuarity ranging from class to a set of classes.
This is a PyTorch-Lightning-based framework, based on our End-to-End Weak Supervision paper (NeurIPS 2021), that allows you to train your favorite neural network for weakly-supervised classification 1