Overview
Proposed by
@InProceedings{glockner_acl18,
author = {Glockner, Max and Shwartz, Vered and Goldberg, Yoav},
title = {Breaking NLI Systems with Sentences that Require Simple Lexical Inferences},
booktitle = {The 56th Annual Meeting of the Association for Computational Linguistics (ACL)},
month = {July},
year = {2018},
address = {Melbourne, Australia}
}
Original dataset available here.
Dataset curation
Labels encoded with the following mapping {"entailment": 0, "neutral": 1, "contradiction": 2}
and made available in the label column.
Code to create the dataset
import pandas as pd
from datasets import Features, Value, ClassLabel, Dataset, Sequence
# load data
with open("<path to folder>/dataset.jsonl", "r") as fl:
data = fl.read().split("\n")
df = pd.DataFrame([eval(i) for i in data if len(i) > 0])
# encode labels
df["label"] = df["gold_label"].map({"entailment": 0, "neutral": 1, "contradiction": 2})
# cast to dataset
features = Features({
"sentence1": Value(dtype="string", id=None),
"category": Value(dtype="string", id=None),
"gold_label": Value(dtype="string", id=None),
"annotator_labels": Sequence(feature=Value(dtype="string", id=None), length=3),
"pairID": Value(dtype="int32", id=None),
"sentence2": Value(dtype="string", id=None),
"label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
})
ds = Dataset.from_pandas(df, features=features)
ds.push_to_hub("breaking_nli", token="<token>", split="all")