README.md

metadata

language:
  - en
multilinguality:
  - monolingual
size_categories:
  - 1M<n<10M
task_categories:
  - feature-extraction
  - sentence-similarity
pretty_name: AllNLI
dataset_info:
  - config_name: pair
    features:
      - name: anchor
        dtype: string
      - name: positive
        dtype: string
    splits:
      - name: train
        num_bytes: 43012118
        num_examples: 314315
      - name: dev
        num_bytes: 992955
        num_examples: 6808
      - name: test
        num_bytes: 1042254
        num_examples: 6831
    download_size: 27501136
    dataset_size: 45047327
  - config_name: pair-class
    features:
      - name: premise
        dtype: string
      - name: hypothesis
        dtype: string
      - name: label
        dtype:
          class_label:
            names:
              '0': entailment
              '1': neutral
              '2': contradiction
    splits:
      - name: train
        num_bytes: 138755142
        num_examples: 942069
      - name: dev
        num_bytes: 3034127
        num_examples: 19657
      - name: test
        num_bytes: 3142127
        num_examples: 19656
    download_size: 72651651
    dataset_size: 144931396
  - config_name: pair-score
    features:
      - name: label
        dtype: float64
      - name: sentence_1
        dtype: string
      - name: sentence_2
        dtype: string
    splits:
      - name: train
        num_bytes: 138755142
        num_examples: 942069
      - name: dev
        num_bytes: 3034127
        num_examples: 19657
      - name: test
        num_bytes: 3142127
        num_examples: 19656
    download_size: 72656563
    dataset_size: 144931396
  - config_name: triplet
    features:
      - name: anchor
        dtype: string
      - name: positive
        dtype: string
      - name: negative
        dtype: string
    splits:
      - name: train
        num_bytes: 197631954
        num_examples: 1115700
      - name: dev
        num_bytes: 2545182
        num_examples: 13168
      - name: test
        num_bytes: 2682532
        num_examples: 13218
    download_size: 65778763
    dataset_size: 202859668
configs:
  - config_name: pair
    data_files:
      - split: train
        path: pair/train-*
      - split: dev
        path: pair/dev-*
      - split: test
        path: pair/test-*
  - config_name: pair-class
    data_files:
      - split: train
        path: pair-class/train-*
      - split: dev
        path: pair-class/dev-*
      - split: test
        path: pair-class/test-*
  - config_name: pair-score
    data_files:
      - split: train
        path: pair-score/train-*
      - split: dev
        path: pair-score/dev-*
      - split: test
        path: pair-score/test-*
  - config_name: triplet
    data_files:
      - split: train
        path: triplet/train-*
      - split: dev
        path: triplet/dev-*
      - split: test
        path: triplet/test-*

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: "sentence_1", "sentence_2", "label"
• Column types: str, str, float
• Examples:

{'premise': 'A person on a horse jumps over a broken down airplane.', 'hypothesis': 'A person is training his horse for a competition.', 'label': 1.0}

• 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 also included.
• Deduplified: Yes