Open-source alternatives to Ml Annotate

YEDDA: A Lightweight Collaborative Text Span Annotation Tool. Code for ACL 2018 Best Demo Paper Nomination.

MAE is a lightweight, general-purpose natural language annotation tool

About Anafora - How Annotation Works - Requirements - Documentation - FAQ - Get Anafora

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

In an attempt to keep all user-facing documentation in one place, please visit the brat homepagebrat which contains extensive documentation and examples of how to use and configure brat. We apologise for only providing minimal documentation along with the installation package but the risk of…

Smarter Manual Annotation for Resource-constrained collection of Training data

You like WebAnno? Use INCEpTION! INCEpTION is the new open source text annotation platform that has all the flexibility and many more exciting features including a completely new human-in-the-loop annotation assistance support, the ability to search texts and annotations, support for RDF/SPARQL…

nlp-recipes is a collection of implementation guides and reference templates for applying natural language processing techniques to real-world tasks. It provides standardized workflows and code examples for developing NLP pipelines, from dataset preparation and model training to performance evaluation. The project focuses on the practical application of transformer-based models, offering patterns for fine-tuning pretrained architectures for tasks such as text classification, named entity recognition, and question answering. It also includes a toolkit for model interpretability, allowing users