Paper Title Triplets for Semantic Similarity

Encompassing triplets of sentences (anchor, positive, negative) derived from paper titles. It is designed for semantic similarity tasks and is tagged for use with sentence similarity classification. The specific number of rows, columns, and file size is unknown.

Use Cases

• Train a semantic similarity model using anchor, positive, and negative sentence triplets for contrastive learning.
• Fine-tune a text embedding model on paper title triplets to improve performance on academic text retrieval.
• Benchmark sentence similarity classification algorithms using the provided triplet structure.

Strengths

• Dataset is tagged with a size category of 100K to 1M entries, indicating a substantial scale.
• Data modality is text, specifically formatted as triplets for semantic similarity tasks.
• The dataset is associated with the SPECTER model and academic paper titles from arXiv.

Limitations

• The exact row count, column structure, and sample data are unavailable, hindering precise assessment.
• The dataset card was created by a third party (Hugging Face team), not the original authors, which may affect documentation accuracy.
• The license is listed as unknown, creating potential uncertainty for commercial use.

Provenance

Source

Hugging Face dataset repository under the author 'embedding-data'.

Collection Method

Triplets of sentences (anchor, positive, negative) derived from paper titles, likely for contrastive learning.

Time Range

Associated with arXiv papers from 2004-07-18 onward, as indicated by the tag 'Arxiv200407180'.

Freshness

Last updated on 2022-08-02.

Geography

Tagged with region 'us', but specific geographic coverage is unknown.