Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
File size: 2,514 Bytes
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---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: pmid
dtype: int64
- name: journal
dtype: string
- name: title
dtype: string
- name: abstract
dtype: string
- name: keywords
dtype: string
- name: pub_type
dtype: string
- name: authors
dtype: string
- name: doi
dtype: string
- name: label
sequence: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 85014595
num_examples: 24960
- name: validation
num_bytes: 9075648
num_examples: 2500
- name: test
num_bytes: 21408810
num_examples: 6239
download_size: 63244210
dataset_size: 115499053
task_categories:
- text-classification
language:
- en
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:** [BioCreative VII LitCovid Track](https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-5/)
- **Paper:** [Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428574/)
### Dataset Summary
Topic annotation in LitCovid is a multi-label document classification task that assigns one or more labels to each article. There are 7 topic labels used in LitCovid: Treatment, Diagnosis, Prevention, Mechanism, Transmission, Epidemic Forecasting, and Case Report. These topics have been demonstrated to be effective for information retrieval and have also been used in many downstream applications related to COVID-19.
## Dataset Structure
### Data Instances and Data Splits
- the training set contains 24,960 articles from LitCovid;
- the validation set contains 6,239 articles from LitCovid;
- the test set contains 2,500 articles from LitCovid;
### Data Fields
with the following fields retrieved from PubMed/LitCovid:
• pmid: PubMed Identifier
• journal: journal name
• title: article title
• abstract: article abstract
• keywords: author-provided keywords
• pub_type: article type, e.g., journal article
• authors: author names
• doi: Digital Object Identifier
• label: annotated topics in list format indicating absence or presence of labels in the order 'Treatment,Diagnosis,Prevention,Mechanism,Transmission,Epidemic Forecasting,Case Report'
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