It is an open-source no-code system for text annotation and building text classifiers. With this, domain experts can quickly create custom Natural Language Processing (NLP) models by themselves, with no dependency on NLP experts. No AI knowledge needed; from task definition to working model in just a few hours!
Label Sleuth is a tool in the Text & Language Models category of a tech stack.
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It is a visual programming language that lets you build a fully-functional web app without writing code. Users have built marketplaces, CRM tools, social networks. Engineers can focus on new features and add them as plugins with code, while business people can focus on the customer-facing product.
It is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages.
It provides general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.
rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries.
Python, Windows, Linux, Anaconda, macOS are some of the popular tools that integrate with Label Sleuth. Here's a list of all 5 tools that integrate with Label Sleuth.