FOL-nli / README.md
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metadata
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
dataset_info:
  features:
    - name: premise
      dtype: string
    - name: hypothesis
      dtype: string
    - name: label
      dtype: string
    - name: premise_tptp
      dtype: string
    - name: hypothesis_tptp
      dtype: string
    - name: proof_inputs
      sequence: string
    - name: proof
      dtype: string
    - name: rule_concentration
      dtype: float64
  splits:
    - name: train
      num_bytes: 545983394.2802362
      num_examples: 82219
    - name: validation
      num_bytes: 68252074.65929127
      num_examples: 10278
    - name: test
      num_bytes: 68245434.06047249
      num_examples: 10277
  download_size: 182483284
  dataset_size: 682480903
license: apache-2.0
task_categories:
  - text-classification
language:
  - en
tags:
  - logic
  - reasoning
  - fol
  - first-order-logic
  - syllogism
  - sorite
size_categories:
  - 100K<n<1M
task_ids:
  - natural-language-inference
  - multi-input-text-classification
source_datasets:
  - original

Dataset Card for "FOL-nli"

https://github.com/sileod/unigram/

https://arxiv.org/abs/2406.11035

Citation:

@article{sileo2024scaling,
  title={Scaling Synthetic Logical Reasoning Datasets with Context-Sensitive Declarative Grammars},
  author={Sileo, Damien},
  journal={arXiv preprint arXiv:2406.11035},
  year={2024}
}