Datasets:
File size: 1,032 Bytes
2b5b400 e89d128 69bdd4f e89d128 69bdd4f e89d128 2b5b400 e89d128 69bdd4f 5296220 69bdd4f 5296220 69bdd4f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 |
---
license: apache-2.0
task_categories:
- question-answering
- text-generation
language:
- en
size_categories:
- 10K<n<100K
---
![image/jpeg](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F6489e1e3eb763749c663f40c%2FZKzKrpYNjiFHLZtH062GW.jpeg%3C%2Fspan%3E)
Reference : "A Question-Entailment Approach to Question Answering". Asma Ben Abacha and Dina Demner-Fushman. BMC Bioinformatics, 2019."
<br/>This is an update of Keivalya Pandya's dataset (keivalya/MedQuad-MedicalQnADataset).
<h1> Content </h1>
There are medical questions and corresponding responses in a prompt format for chat or instruct model types
<br/>In order to fine tuned LLM with small HW (1 or 2 GPU with 14 Go)
<br/>Rows above 128 tokens have been deleted.
<br/>Rows have been truncated to a line break or a sentence end in order to keep a correct meaning
<h2> Script to download the dataset </h2>
<br/>from datasets import load_dataset
<br/>dataset_name = "Laurent1/MedQuad-MedicalQnADataset_128tokens_max"
<br/>dataset = load_dataset(dataset_name, split="train") |