sentence-transformers/all-nli

Dataset Card for AllNLI

This dataset is a concatenation of the SNLI and MultiNLI datasets. Despite originally being intended for Natural Language Inference (NLI), this dataset can be used for training/finetuning an embedding model for semantic textual similarity.

Dataset Subsets

pair-class subset

• —Columns: "premise", "hypothesis", "label"
• —Column types: str, str, class with {"0": "entailment", "1": "neutral", "2", "contradiction"}
• —Examples:

    {
      'premise': 'A person on a horse jumps over a broken down airplane.',
      'hypothesis': 'A person is training his horse for a competition.',
      'label': 1,
    }

• —Collection strategy: Reading the premise, hypothesis and integer label from SNLI & MultiNLI datasets.
• —Deduplified: Yes

pair-score subset

• —Columns: "sentence1", "sentence2", "score"
• —Column types: str, str, float
• —Examples:

    {
      'sentence1': 'A person on a horse jumps over a broken down airplane.',
      'sentence2': 'A person is training his horse for a competition.',
      'score': 0.5,
    }

• —Collection strategy: Taking the pair-class subset and remapping "entailment", "neutral" and "contradiction" to 1.0, 0.5 and 0.0, respectively.
• —Deduplified: Yes

pair subset

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

    {
      'anchor': 'A person on a horse jumps over a broken down airplane.',
      'positive': 'A person is training his horse for a competition.',
    }

• —Collection strategy: Reading the SNLI & MultiNLI datasets and considering the "premise" as the "anchor" and the "hypothesis" as the "positive" if the label is "entailment". The reverse ("entailment" as "anchor" and "premise" as "positive") is not included.
• —Deduplified: Yes

triplet subset

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

    {
      'anchor': 'A person on a horse jumps over a broken down airplane.',
      'positive': 'A person is outdoors, on a horse.',
      'negative': 'A person is at a diner, ordering an omelette.',
    }

• —Collection strategy: Reading the SNLI & MultiNLI datasets, for each "premise" making a list of entailing and contradictory sentences using the dataset labels. Then, considering all possible triplets out of these entailing and contradictory lists. The reverse ("entailment" as "anchor" and "premise" as "positive") is not included.
• —Deduplified: Yes