pietrolesci/recast_white

## Overview

This dataset has been introduced by "Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework", Aaron Steven White, Pushpendre Rastogi, Kevin Duh, Benjamin Van Durme. IJCNLP, 2017. Original data available [here](https://github.com/decompositional-semantics-initiative/DNC/raw/master/inference_is_everything.zip).

## Dataset curation
The following processing is applied

- `hypothesis_grammatical` and `judgement_valid` columns are filled with `""` when empty
- all columns are stripped
- the `entailed` column is renamed `label`
- `label` column is encoded with the following mapping `{"not-entailed": 0, "entailed": 1}`
- columns `rating` and `good_word` are dropped from `fnplus` dataset

## Code to generate the dataset

```python
import pandas as pd
from datasets import Features, Value, ClassLabel, Dataset, DatasetDict

ds = {}
for name in ("fnplus", "sprl", "dpr"):

# read data
with open(f"<path to files>/{name}_data.txt", "r") as f:
data = f.read()
data = data.split("\n\n")
data = [lines.split("\n") for lines in data]
data = [dict([col.split(":", maxsplit=1) for col in line if len(col) > 0]) for line in data]
df = pd.DataFrame(data)

# fill empty hypothesis_grammatical and judgement_valid
df["hypothesis_grammatical"] = df["hypothesis_grammatical"].fillna("")
df["judgement_valid"] = df["judgement_valid"].fillna("")

# fix dtype
df["index"] = df["index"].astype(int)

# strip
for col in df.select_dtypes(object).columns:
df[col] = df[col].str.strip()

# rename columns
df = df.rename(columns={"entailed": "label"})

# encode labels
df["label"] = df["label"].map({"not-entailed": 0, "entailed": 1})

# cast to dataset
features = Features({
"provenance": Value(dtype="string", id=None),
"index": Value(dtype="int64", id=None),
"text": Value(dtype="string", id=None),
"hypothesis": Value(dtype="string", id=None),
"partof": Value(dtype="string", id=None),
"hypothesis_grammatical": Value(dtype="string", id=None),
"judgement_valid": Value(dtype="string", id=None),
"label": ClassLabel(num_classes=2, names=["not-entailed", "entailed"]),
})

# select common columns
df = df.loc[:, list(features.keys())]
ds[name] = Dataset.from_pandas(df, features=features)

ds = DatasetDict(ds)
ds.push_to_hub("recast_white", token="<token>")
```

原始信息汇总

数据集概述

本数据集由Aaron Steven White, Pushpendre Rastogi, Kevin Duh, Benjamin Van Durme在IJCNLP 2017中发表的论文"Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework"引入。原始数据可在此链接获取：https://github.com/decompositional-semantics-initiative/DNC/raw/master/inference_is_everything.zip。

数据集处理

• 对hypothesis_grammatical和judgement_valid列中的空值填充为""。
• 所有列进行strip处理。
• 将entailed列重命名为label。
• 对label列进行编码，映射为{"not-entailed": 0, "entailed": 1}。
• 从fnplus数据集中删除rating和good_word列。

数据集生成代码

数据集通过以下步骤生成：

1. 读取数据文件，将数据分割并转换为字典列表，再转换为DataFrame。
2. 填充hypothesis_grammatical和judgement_valid列的空值。
3. 修正index列的数据类型为整数。
4. 对所有文本类型列进行strip处理。
5. 重命名entailed列为label。
6. 对label列进行编码，映射为二分类标签。
7. 定义数据集的特征结构。
8. 选择特征结构中的列。
9. 将DataFrame转换为Dataset，并上传至数据集中心。