qbao775/PARARULE-Plus-Depth-2

---
license: mit
task_categories:
- text-classification
- question-answering
language:
- en
tags:
- Reasoning
- Multi-Step-Deductive-Reasoning
- Logical-Reasoning
size_categories:
- 100K<n<1M
---
# PARARULE-Plus-Depth-2
This is a branch which includes the dataset from PARARULE-Plus Depth=2. PARARULE Plus is a deep multi-step reasoning dataset over natural language. It can be seen as an improvement on the dataset of PARARULE (Peter Clark et al., 2020). Both PARARULE and PARARULE-Plus follow the closed-world assumption and negation as failure. The motivation is to generate deeper PARARULE training samples. We add more training samples for the case where the depth is greater than or equal to two to explore whether Transformer has reasoning ability. PARARULE Plus is a combination of two types of entities, animals and people, and corresponding relationships and attributes. From the depth of 2 to the depth of 5, we have around 100,000 samples in the depth of each layer, and there are nearly 400,000 samples in total.

Here is the original links for PARARULE-Plus including paper, project and data.

Paper: https://www.cs.ox.ac.uk/isg/conferences/tmp-proceedings/NeSy2022/paper15.pdf

Project: https://github.com/Strong-AI-Lab/Multi-Step-Deductive-Reasoning-Over-Natural-Language

Data: https://github.com/Strong-AI-Lab/PARARULE-Plus

PARARULE-Plus has been collected and merged by [LogiTorch.ai](https://www.logitorch.ai/), [ReasoningNLP](https://github.com/FreedomIntelligence/ReasoningNLP), [Prompt4ReasoningPapers](https://github.com/zjunlp/Prompt4ReasoningPapers) and [OpenAI/Evals](https://github.com/openai/evals/pull/651).

In this huggingface version, we pre-processed the dataset and use `1` to represent `true` and `0` to represent `false` to better help user train model.

## How to load the dataset?
```
from datasets import load_dataset
dataset = load_dataset("qbao775/PARARULE-Plus-Depth-2")
```

## How to train a model using the dataset?

We provide an [example](https://github.com/Strong-AI-Lab/PARARULE-Plus/blob/main/README.md#an-example-script-to-load-pararule-plus-and-fine-tune-bert) that you can `git clone` the project and fine-tune the dataset locally.

## Citation
```
@inproceedings{bao2022multi,
title={Multi-Step Deductive Reasoning Over Natural Language: An Empirical Study on Out-of-Distribution Generalisation},
author={Qiming Bao and Alex Yuxuan Peng and Tim Hartill and Neset Tan and Zhenyun Deng and Michael Witbrock and Jiamou Liu},
year={2022},
publisher={The 2nd International Joint Conference on Learning and Reasoning and 16th International Workshop on Neural-Symbolic Learning and Reasoning (IJCLR-NeSy 2022)}
}
```

原始信息汇总

PARARULE-Plus-Depth-2 数据集概述

基本信息

• 许可证：MIT
• 任务类别：
  • 文本分类
  • 问答
• 语言：英语
• 标签：
  • 推理
  • 多步骤演绎推理
  • 逻辑推理
• 数据规模：100K<n<1M

数据集描述

PARARULE-Plus-Depth-2 是一个深度多步骤推理数据集，基于 PARARULE-Plus Depth=2。PARARULE Plus 是对 PARARULE 数据集的改进，遵循封闭世界假设和失败即否定原则。该数据集的目的是生成更深层次的 PARARULE 训练样本，特别是针对深度大于或等于二的样本。PARARULE Plus 包含两种类型的实体（动物和人）及其相应的关系和属性。从深度2到深度5，每个层次约有100,000个样本，总计近400,000个样本。

数据加载

python from datasets import load_dataset dataset = load_dataset("qbao775/PARARULE-Plus-Depth-2")

引用

@inproceedings{bao2022multi, title={Multi-Step Deductive Reasoning Over Natural Language: An Empirical Study on Out-of-Distribution Generalisation}, author={Qiming Bao and Alex Yuxuan Peng and Tim Hartill and Neset Tan and Zhenyun Deng and Michael Witbrock and Jiamou Liu}, year={2022}, publisher={The 2nd International Joint Conference on Learning and Reasoning and 16th International Workshop on Neural-Symbolic Learning and Reasoning (IJCLR-NeSy 2022)} }