metadata

license: apache-2.0
task_categories:
  - question-answering
  - text-generation
language:
  - en
size_categories:
  - 10K<n<100K

Reference : "A Question-Entailment Approach to Question Answering". Asma Ben Abacha and Dina Demner-Fushman. BMC Bioinformatics, 2019."
This is an update of Keivalya Pandya's dataset (keivalya/MedQuad-MedicalQnADataset).

Content

There are medical questions and corresponding responses in a prompt format for chat or instruct model types
In order to fine tuned LLM with small HW (1 or 2 GPU with 14 Go)
Rows above 128 tokens have been deleted.
Rows have been truncated to a line break or a sentence end in order to keep a correct meaning

Script to download the dataset

from datasets import load_dataset
dataset_name = "Laurent1/MedQuad-MedicalQnADataset_128tokens_max"
dataset = load_dataset(dataset_name, split="train")