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text-labeling
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Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets
Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.
Collaborate & label any type of data, images, text, or documents, in an easy web interface or desktop app.
Data labeling react app that is backend agnostic and can be embedded into your applications — distributed as an NPM package
A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI <img alt="octocat" src="https://github.githubassets.com/images/icons/emoji/octocat.png?v8" />)
🚤 Label data at scale. Fun and precision included.
Simplest and fastest image and text annotation tool.
Label data using HuggingFace's transformers and automatically get a prediction service
Alternate Implementation for Zero Shot Text Classification: Instead of reframing NLI/XNLI, this reframes the text backbone of CLIP models to do ZSC. Hence, can be lightweight + supports more languages without trading-off accuracy. (Super simple, a 10th-grader could totally write this but since no 10th-grader did, I did) - Prithivi Da
LaMa, short for Labelling Machine, is an web application developed for aiding in thematic analysis of qualitative data.
Text labelling desktop application
Minimalistic CLI labeling tool for text classification
Large-Scale text analysis using generative language models: A case study in discovering public value expressions in AI patents. Code and data.
🚀SpAnnor annotator for Named Entity Recognition easy to use tool. The annotator allows users to quickly assign custom labels to one or more entities in the text. Easy to setup for Data Training for SpaCy 🔥.
For learning. Collecting techniques of each step from knowledge graph building processes.
Web applications for human annotation on documents
This project classifies BBC News articles into five topics—Sport, Business, Politics, Tech, and Entertainment—using Naïve Bayes, Random Forest, and SVM. Feature extraction with TF-IDF and Bag of Words improves topic classification efficiency for enhanced information management.
