This repository provides functionality and documentation for using IIC ML products via AWS Sagemaker Marketplace.
Rigoembeddings is a text embedding model in Spanish that excels in its accuracy and ability to capture the semantic meaning of words in various contexts. It can be used as a building block for advanced NLP applications and benefits across a wide range of fields.
The main applications of this model include NLP, where it enhances text comprehension and generation, facilitating the creation of chatbots, Retrieval-Augmented Generation (RAG), virtual assistants, and recommendation systems among others.
RigoChat-7b is the second version of RigoChat, a family of Large Language Models (LLMs) designed to solve typical NLP tasks with Spanish instructions such as Tool Use, Summarization, Math, Code, Abstractive-QA, etc.
This model has no specific use case and can be applied to a wide range of tasks. Indeed, it offers an improvement for generalist tasks in Spanish, particularly in RAG (Retriever Augmented Generation) systems with Spanish databases, as its training focused on resolving questions about contexts to prevent hallucinations and ensure safe responses.
ERAS is an easy reading analyzer for Spanish texts. With four global indexes and thirty subindexes, it evaluates text linguistic features and allows to easily detect readability improvement areas.
It uses NLP advanced analysis to automatically spot linguistic aspects to be improved for the sake of information accessibility. ERAS can be used as an easy reading text writing assistant, reducing easy reading adaptation time and as an easy reading texts quality evaluator.
LUCES is a clear communication analyzer for Spanish texts. With three global indexes and thirteen subindexes, it evaluates text linguistic features and allows to easily detect clarity improvement areas.
It uses NLP advanced analysis to automatically spot linguistic aspects to be improved for the sake of information transparency. LUCES can be used as a text writing assistant and as a text clarity evaluator, maximizing content understandability in both cases.