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Jerjes/neuro-specter2-triplets-multi-pool

Jerjes/neuro-specter2-triplets-multi-pool This dataset contains anchor papers with their top-K most similar (positive) and most dissimilar (negative) papers based on SPECTER2 embeddings. Dataset Structure Each row contains: anchor_id: Unique identifier for the anchor paper anchor_title: Title of the anchor paper anchor_abstract: Abstract of the anchor paper positive_pool: List of 5 most similar papers, each as [id, title, abstract] negative_pool: List of 5 most… See the full description on the dataset page: https://huggingface.co/datasets/Jerjes/neuro-specter2-triplets-multi-pool.

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Jerjes/neuro-specter2-triplets-multi-pool

This dataset contains anchor papers with their top-K most similar (positive) and most dissimilar (negative) papers based on SPECTER2 embeddings.

Dataset Structure

Each row contains:

  • —anchor_id: Unique identifier for the anchor paper
  • —anchor_title: Title of the anchor paper
  • —anchor_abstract: Abstract of the anchor paper
  • —positive_pool: List of 5 most similar papers, each as [id, title, abstract]
  • —negative_pool: List of 5 most dissimilar papers, each as [id, title, abstract]

Dataset Statistics

  • —Total anchors: 100,000
  • —Positives per anchor: 5
  • —Negatives per anchor: 5
  • —Embedding model: allenai/specter2_base

Usage

python
from datasets import load_dataset

dataset = load_dataset("Jerjes/neuro-specter2-triplets-multi-pool")

# Access a sample
sample = dataset["train"][0]
print(f"Anchor: {sample['anchor_title']}")
print(f"Top positive: {sample['positive_pool'][0][1]}")  # title of most similar paper
print(f"Top negative: {sample['negative_pool'][0][1]}")  # title of most dissimilar paper

Citation

If you use this dataset, please cite the original SPECTER2 paper:

@inproceedings{specter2,
    title={SPECTER2: Better Scientific Paper Representations Through Augmented Word Embeddings},
    author={Pradeep Dasigi and Kyle Lo and Iz Beltagy and Arman Cohan and Noah A. Smith and Matt Gardner},
    booktitle={Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
    year={2021}
}