Datasets:
language:
- en
license: mit
task_categories:
- sentence-similarity
task_ids:
- semantic-similarity-classification
paperswithcode_id: embedding-data/SPECTER
pretty_name: SPECTER
tags:
- paraphrase-mining
Dataset Card for "SPECTER"
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
Dataset containing triplets (three sentences): anchor, positive, and negative. Contains titles of papers.
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.
Dataset Structure
Each example in the dataset contains triplets of equivalent sentences and is formatted as a dictionary with the key "set" and a list with the sentences as "value".
Each example is a dictionary with a key, "set", containing a list of three sentences (anchor, positive, and negative):
{"set": [anchor, positive, negative]}
{"set": [anchor, positive, negative]}
...
{"set": [anchor, positive, negative]}
This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train models using triplets.
Usage Example
Install the 🤗 Datasets library with pip install datasets and load the dataset from the Hub with:
from datasets import load_dataset
dataset = load_dataset("embedding-data/SPECTER")
The dataset is loaded as a DatasetDict and has the format:
DatasetDict({
train: Dataset({
features: ['set'],
num_rows: 684100
})
})
Review an example i with:
dataset["train"][i]["set"]