QuAC contains 98,407 QA pairs from 13,594 dialogs. The dialogs were conducted on 8,854 unique sections from 3,611 unique Wikipedia articles, and every dialog ...

18 янв. 2024 г. · Datasets: · allenai. /. quac. like 28. Follow. Ai2 1,569 ; Tasks: Question Answering · Text Generation · Fill-Mask ; Sub-tasks: dialogue-modeling.

Question Answering in Context is a large-scale dataset that consists of around 14K crowdsourced Question Answering dialogs with 98K question-answer pairs in ...

This dataset can be used for training and evaluating models on tasks related to follow-up question generation or understanding in conversational contexts.

This is the SciBERT language representation model fine tuned for Question Answering. SciBERT is a pre-trained language model based on BERT that has been trained ...

Datasets: · allenai. /. quac. like 28. Follow. Ai2 1,573 ; Tasks: Question Answering · Text Generation · Fill-Mask ; Sub-tasks: dialogue-modeling · extractive-qa.

Datasets: · allenai. /. quac. like 28. Follow. Ai2 1,400 ; Tasks: Question Answering · Text Generation · Fill-Mask ; Sub-tasks: dialogue-modeling · extractive-qa.

QuAC (Question Answering in Context) is a question-answering in-context dataset containing 14K information-seeking QA conversations (100K questions in total).

21 авг. 2018 г. · We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total).

28 июн. 2022 г. · Question Answering in Context is a dataset for modeling, understanding, and participating in information seeking dialog.