README.md

metadata

language:
  - en
multilinguality:
  - monolingual
size_categories:
  - 100K<n<1M
task_categories:
  - feature-extraction
  - sentence-similarity
pretty_name: Specter
tags:
  - sentence-transformers
dataset_info:
  - config_name: pair
    features:
      - name: anchor
        dtype: string
      - name: positive
        dtype: string
    splits:
      - name: train
        num_bytes: 55252049
        num_examples: 380142
    download_size: 24217449
    dataset_size: 55252049
  - config_name: triplet
    features:
      - name: anchor
        dtype: string
      - name: positive
        dtype: string
      - name: negative
        dtype: string
    splits:
      - name: train
        num_bytes: 152814049
        num_examples: 684098
    download_size: 62182004
    dataset_size: 152814049
configs:
  - config_name: pair
    data_files:
      - split: train
        path: pair/train-*
  - config_name: triplet
    data_files:
      - split: train
        path: triplet/train-*

Dataset Card for Specter

This dataset is a collection of title-related-unrelated triplets from Scientific Publications on Specter. See Specter for additional information. This dataset can be used directly with Sentence Transformers to train embedding models.

Dataset Subsets

triplet subset

• Columns: "anchor", "positive", "negative"
• Column types: str, str, str
• Examples:

  {
    'anchor': "Integrating children's contributions in the interaction design process",
    'positive': 'Designing for or designing with? Informant design for interactive learning environments',
    'negative': 'Power Operation in ISD: Technological Frames Perspectives.',
  }
  

• Collection strategy: Reading the Specter dataset from embedding-training-data, followed by deduplication.
• Deduplified: Yes

pair subset

• Columns: "anchor", "positive"
• Column types: str, str
• Examples:

  {
    'anchor': 'Time-dependent trajectory regression on road networks via multi-task learning',
    'positive': 'Convex multi-task feature learning',
  }
  

• Collection strategy: Reading the Specter dataset from embedding-training-data, only taking the title and related title, and then performing deduplication.
• Deduplified: Yes