license: apache-2.0 | |
configs: | |
- config_name: default | |
data_files: | |
- split: train | |
path: data/train-* | |
- split: test | |
path: data/test-* | |
- split: validation | |
path: data/validation-* | |
dataset_info: | |
features: | |
- name: A | |
dtype: string | |
- name: B | |
dtype: string | |
- name: E | |
dtype: int64 | |
- name: H1 | |
dtype: int64 | |
- name: H2 | |
dtype: int64 | |
- name: H3 | |
dtype: int64 | |
- name: label | |
dtype: string | |
splits: | |
- name: train | |
num_bytes: 9803153 | |
num_examples: 99876 | |
- name: test | |
num_bytes: 550241 | |
num_examples: 5000 | |
- name: validation | |
num_bytes: 548346 | |
num_examples: 5000 | |
download_size: 2505053 | |
dataset_size: 10901740 | |
https://github.com/google-deepmind/logical-entailment-dataset | |
``` | |
@inproceedings{ | |
evans2018can, | |
title={Can Neural Networks Understand Logical Entailment?}, | |
author={Richard Evans and David Saxton and David Amos and Pushmeet Kohli and Edward Grefenstette}, | |
booktitle={International Conference on Learning Representations}, | |
year={2018}, | |
url={https://openreview.net/forum?id=SkZxCk-0Z}, | |
} | |
``` | |