Datasets:
Tasks:
Sentence Similarity
Modalities:
Text
Formats:
json
Sub-tasks:
semantic-similarity-classification
Languages:
English
Size:
100K - 1M
ArXiv:
License:
File size: 4,839 Bytes
d426d82 43ee527 d282209 43ee527 bdfd116 43ee527 bdfd116 43ee527 bdfd116 43ee527 bdfd116 43ee527 bdfd116 1ee3bdc 43ee527 bdfd116 43ee527 bdfd116 43ee527 bdfd116 43ee527 bdfd116 43ee527 bdfd116 43ee527 bdfd116 43ee527 bdfd116 43ee527 bdfd116 43ee527 |
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 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 |
---
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)
### 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
[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)
|