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# Jerjes/neuro-specter2-sample-data
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**: 288
- **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-sample-data")
# 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}
}
```
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