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--- |
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language: |
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- en |
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multilinguality: |
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- monolingual |
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size_categories: |
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- 1M<n<10M |
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task_categories: |
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- feature-extraction |
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- sentence-similarity |
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pretty_name: AllNLI |
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tags: |
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- sentence-transformers |
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dataset_info: |
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- config_name: pair |
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features: |
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- name: anchor |
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dtype: string |
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- name: positive |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 43012118 |
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num_examples: 314315 |
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- name: dev |
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num_bytes: 992955 |
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num_examples: 6808 |
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- name: test |
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num_bytes: 1042254 |
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num_examples: 6831 |
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download_size: 27501136 |
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dataset_size: 45047327 |
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- config_name: pair-class |
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features: |
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- name: premise |
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dtype: string |
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- name: hypothesis |
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dtype: string |
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- name: label |
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dtype: |
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class_label: |
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names: |
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'0': entailment |
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'1': neutral |
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'2': contradiction |
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splits: |
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- name: train |
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num_bytes: 138755142 |
|
num_examples: 942069 |
|
- name: dev |
|
num_bytes: 3034127 |
|
num_examples: 19657 |
|
- name: test |
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num_bytes: 3142127 |
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num_examples: 19656 |
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download_size: 72651651 |
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dataset_size: 144931396 |
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- config_name: pair-score |
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features: |
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- name: sentence1 |
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dtype: string |
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- name: sentence2 |
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dtype: string |
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- name: label |
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dtype: float64 |
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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: 72652661 |
|
dataset_size: 144931396 |
|
- config_name: triplet |
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features: |
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- name: anchor |
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dtype: string |
|
- name: positive |
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dtype: string |
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- name: negative |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 98815977 |
|
num_examples: 557850 |
|
- name: dev |
|
num_bytes: 1272591 |
|
num_examples: 6584 |
|
- name: test |
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num_bytes: 1341266 |
|
num_examples: 6609 |
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download_size: 39988980 |
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dataset_size: 101429834 |
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configs: |
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- config_name: pair |
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data_files: |
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- split: train |
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path: pair/train-* |
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- split: dev |
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path: pair/dev-* |
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- split: test |
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path: pair/test-* |
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- config_name: pair-class |
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data_files: |
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- split: train |
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path: pair-class/train-* |
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- split: dev |
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path: pair-class/dev-* |
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- split: test |
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path: pair-class/test-* |
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- config_name: pair-score |
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data_files: |
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- split: train |
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path: pair-score/train-* |
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- split: dev |
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path: pair-score/dev-* |
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- split: test |
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path: pair-score/test-* |
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- config_name: triplet |
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data_files: |
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- split: train |
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path: triplet/train-* |
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- split: dev |
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path: triplet/dev-* |
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- split: test |
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path: triplet/test-* |
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--- |
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|
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# Dataset Card for AllNLI |
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|
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This dataset is a concatenation of the [SNLI](https://huggingface.co/datasets/stanfordnlp/snli) and [MultiNLI](https://huggingface.co/datasets/nyu-mll/multi_nli) datasets. |
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Despite originally being intended for Natural Language Inference (NLI), this dataset can be used for training/finetuning an embedding model for semantic textual similarity. |
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|
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## Dataset Subsets |
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|
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### `pair-class` subset |
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|
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* Columns: "premise", "hypothesis", "label" |
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* Column types: `str`, `str`, `class` with `{"0": "entailment", "1": "neutral", "2", "contradiction"}` |
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* Examples: |
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```python |
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{ |
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'premise': 'A person on a horse jumps over a broken down airplane.', |
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'hypothesis': 'A person is training his horse for a competition.', |
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'label': 1, |
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} |
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``` |
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* Collection strategy: Reading the premise, hypothesis and integer label from SNLI & MultiNLI datasets. |
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* Deduplified: Yes |
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|
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### `pair-score` subset |
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|
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* Columns: "sentence1", "sentence2", "label" |
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* Column types: `str`, `str`, `float` |
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* Examples: |
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```python |
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{ |
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'sentence1': 'A person on a horse jumps over a broken down airplane.', |
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'sentence2': 'A person is training his horse for a competition.', |
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'label': 1.0, |
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} |
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``` |
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* Collection strategy: Taking the `pair-class` subset and remapping "entailment", "neutral" and "contradiction" to 1.0, 0.5 and 0.0, respectively. |
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* Deduplified: Yes |
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|
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### `pair` subset |
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|
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* Columns: "anchor", "positive" |
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* Column types: `str`, `str` |
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* Examples: |
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```python |
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{ |
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'anchor': 'A person on a horse jumps over a broken down airplane.', |
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'positive': 'A person is training his horse for a competition.', |
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} |
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``` |
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* 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. |
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* Deduplified: Yes |
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|
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### `triplet` subset |
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|
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* Columns: "anchor", "positive", "negative" |
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* Column types: `str`, `str`, `str` |
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* Examples: |
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```python |
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{ |
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'anchor': 'A person on a horse jumps over a broken down airplane.', |
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'positive': 'A person is outdoors, on a horse.', |
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'negative': 'A person is at a diner, ordering an omelette.', |
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} |
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``` |
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* 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. |
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* Deduplified: Yes |