## Overview
The original dataset can be found [here](https://github.com/swarnaHub/ConjNLI). It has been
proposed in [ConjNLI: Natural Language Inference Over Conjunctive Sentences](https://aclanthology.org/2020.emnlp-main.661/).

This dataset is a stress test for natural language inference over conjunctive sentences, 
where the premise differs from the hypothesis by conjuncts removed, added, or replaced.


## Dataset curation
No curation is performed. This dataset is "as-is". The label mapping is the usual `{"entailment": 0, "neutral": 1, "contradiction": 2}`
used in NLI datasets.


## Code to create the dataset
```python
import pandas as pd
from datasets import Dataset, ClassLabel, Value, Features, DatasetDict

# download data from repo https://github.com/swarnaHub/ConjNLI
paths = {
    "train": "<path_to_folder>/ConjNLI-master/data/NLI/adversarial_train_15k.tsv",
    "dev": "<path_to_folder>/ConjNLI-master/data/NLI/conj_dev.tsv",
    "test": "<path_to_folder>/ConjNLI-master/data/NLI/conj_test.tsv",
}

if __name__ =="__main__":
    dataset_splits = {}    
    for split, path in paths.items():
        
        # read dataset split
        df = pd.read_csv(paths[split], sep="\t")
    
        # encode labels using the default mapping used by other nli datasets
        # i.e, entailment: 0, neutral: 1, contradiction: 2
        df.columns = df.columns.str.lower()
        if not "test" in path:
            df["label"] = df["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2})
        else:
            df["label"] = -1
    
        # cast to dataset
        features = Features({
            "premise": Value(dtype="string", id=None),
            "hypothesis": Value(dtype="string", id=None),
            "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
        })
        dataset = Dataset.from_pandas(df, features=features)
        dataset_splits[split] = dataset
    
    conj_nli = DatasetDict(dataset_splits)
    conj_nli.push_to_hub("pietrolesci/conj_nli", token="<token>")

```