Create README.md

README.md

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+## Overview
+
+Proposed by
+```latex
+@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](https://github.com/BIU-NLP/Breaking_NLI).
+
+
+## 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
+```python
+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")
+```