---
license: mit
language:
- en
paperswithcode_id: embedding-data/SPECTER
pretty_name: SPECTER
---

# Dataset Card for "ESPECTER"

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)
  
## Dataset Description

- **Homepage:** [https://github.com/allenai/specter](https://github.com/allenai/specter)
- **Repository:** [More Information Needed](https://github.com/allenai/specter/blob/master/README.md)
- **Paper:** [More Information Needed](https://arxiv.org/pdf/2004.07180.pdf)
- **Point of Contact:** [@armancohan](https://github.com/armancohan), [@sergeyf](https://github.com/sergeyf), [@haroldrubio](https://github.com/haroldrubio), [@jinamshah](https://github.com/jinamshah)
- **Size of downloaded dataset files:**
- **Size of the generated dataset:** 
- **Total amount of disk used:** 38.3 MB

### 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.

### Supported Tasks and Leaderboards

[More Information Needed](https://github.com/allenai/specter)

### Languages

[More Information Needed](https://github.com/allenai/specter)

## 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

[More Information Needed](https://github.com/allenai/specter)

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed](https://github.com/allenai/specter)

#### Who are the source language producers?

[More Information Needed](https://github.com/allenai/specter)

### Annotations

#### Annotation process

[More Information Needed](https://github.com/allenai/specter)

#### Who are the annotators?

[More Information Needed](https://github.com/allenai/specter)

### Personal and Sensitive Information

[More Information Needed](https://github.com/allenai/specter)

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed](https://github.com/allenai/specter)

### Discussion of Biases

[More Information Needed](https://github.com/allenai/specter)

### Other Known Limitations

[More Information Needed](https://github.com/allenai/specter)

## Additional Information

### Dataset Curators

[More Information Needed](https://github.com/allenai/specter)

### Licensing Information

[More Information Needed](https://github.com/allenai/specter)

### 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:

- [https://github.com/allenai/scidocs](https://github.com/allenai/scidocs)


### Contributions

Thanks to [@armancohan](https://github.com/armancohan), [@sergeyf](https://github.com/sergeyf), [@haroldrubio](https://github.com/haroldrubio), [@jinamshah](https://github.com/jinamshah)