#自然语言处理#Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets
#自然语言处理#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.
翻译 - Kashgari是用于文本标签和文本分类的可立即投入生产的NLP Transfer学习框架，其中包括Word2Vec，BERT和GPT2语言嵌入。
#计算机科学#Collaborate & label any type of data, images, text, or documents, in an easy web interface or desktop app.
翻译 - 在简单的Web界面或桌面应用程序中协作并标记任何类型的数据，图像，文本或文档。
Data labeling react app that is backend agnostic and can be embedded into your applications — distributed as an NPM package
#自然语言处理#A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI 
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#计算机科学#🚤 Label data at scale. Fun and precision included.
Simplest and fastest image and text annotation tool.
#自然语言处理#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...
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.
#自然语言处理#🚀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 S...
#自然语言处理#For learning. Collecting techniques of each step from knowledge graph building processes.
#自然语言处理#Web applications for human annotation on documents
#自然语言处理#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...