Datasets:
license: mit
language:
- en
paperswithcode_id: embedding-data/SPECTER
pretty_name: SPECTER
Dataset Card for "ESPECTER"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://github.com/allenai/specter
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: @armancohan, @sergeyf, @haroldrubio, @jinamshah
Dataset Summary
SPECTER: Document-level Representation Learning using Citation-informed Transformers. A new method to generate document-level embedding of scientific documents based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fine-tuning.
Disclaimer: The team releasing SPECTER did not upload the dataset to the Hub and did not write a dataset card. These steps were done by the Hugging Face team.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Specter requires two main files as input to embed the document.
A text file with ids of the documents you want to embed and a json metadata file
consisting of the title and abstract information.
Sample files are provided in the data/ directory to get you started.
Input data format is according to:
metadata.json format:
{
'doc_id': {'title': 'representation learning of scientific documents',
'abstract': 'we propose a new model for representing abstracts'},
}
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@inproceedings{specter2020cohan,
title={{SPECTER: Document-level Representation Learning using Citation-informed Transformers}},
author={Arman Cohan and Sergey Feldman and Iz Beltagy and Doug Downey and Daniel S. Weld},
booktitle={ACL},
year={2020}
}
SciDocs benchmark
SciDocs evaluation framework consists of a suite of evaluation tasks designed for document-level tasks.
Link to SciDocs:
Contributions
Thanks to @armancohan, @sergeyf, @haroldrubio, @jinamshah