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## 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. Note that labels for `test` split are not available.
Also, the `train` split is originally named `adversarial_train_15k`.
Note that there are 2 instances (join on "premise", "hypothesis", "label") present both in `train` and `dev`.
## 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",
}
dataset_splits = {}
for split, path in paths.items():
# load data
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>")
# check overlap between splits
from itertools import combinations
for i, j in combinations(conj_nli.keys(), 2):
print(
f"{i} - {j}: ",
pd.merge(
conj_nli[i].to_pandas(),
conj_nli[j].to_pandas(),
on=["premise", "hypothesis", "label"], how="inner"
).shape[0],
)
#> train - dev: 2
#> train - test: 0
#> dev - test: 0
``` |