clinc/oos-eval

A collection of task-specific NLU datasets

We introduce MKQA, an open-domain question answering evaluation set comprising 10k question-answer pairs aligned across 26 typologically diverse languages (260k question-answer pairs in total). The goal of this dataset is to provide a challenging benchmark for question answering quality across a wide set of languages. Please refer to our paper for details, MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering

Public & Community-shared datasets for Aspect-based sentiment analysis and Text Classification

Repo for the Belebele dataset, a massively multilingual reading comprehension dataset.

A powerful tool for creating datasets for LLM fine-tuning 、RAG and Eval

A framework for training and evaluating AI models on a variety of openly available dialogue datasets.

Open-Domain Question Answering Goes Conversational via Question Rewriting