SelQA Dataset
Created by Jurczyk et al. at 2016, the SelQA Dataset provides crowdsourced annotation for two selection-based question answer tasks, answer sentence selection and answer triggering. Our dataset composes about 8K factoid questions for the top-10 most prevalent topics among Wikipedia articles., in English language. Containing 8 in JSON, TSV file format.
Dataset Sources
Here you can download the SelQA dataset in JSON, TSV format.
Download SelQA dataset JSON, TSV files
Fine-tune with SelQA dataset
Metatext is a powerful no-code tool for train, tune and integrate custom NLP models
Paper
Read full original SelQA paper.
Classify and extract text 10x better and faster 🦾
Metatext helps you to classify and extract information from text and documents with customized language models with your data and expertise.