Create README.md

README.md

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+## Overview
+Original dataset [here](https://github.com/felipessalvatore/NLI_datasets).
+
+
+## Dataset curation
+One hypothesis in the dev set and three hypotheses in the train set are empty and have been
+filled in with the empty string `""`. Labels are encoded with custom NLI mapping, that is
+
+```
+{"entailment": 0, "neutral": 1, "contradiction": 2}
+```
+
+
+## Code to create the dataset
+```python
+import pandas as pd
+from datasets import Features, Value, ClassLabel, Dataset, DatasetDict, load_dataset
+from pathlib import Path
+
+
+# load datasets
+path = Path("<path to folder>/nli_datasets")
+datasets = {}
+for dataset_path in path.iterdir():
+    datasets[dataset_path.name] = {}
+    for name in dataset_path.iterdir():
+        df = pd.read_csv(name)
+        datasets[dataset_path.name][name.name.split(".")[0]] = df
+
+
+ds = {}
+for name, df_ in datasets["fracas"].items():
+
+    df = df_.copy()
+    assert df["label"].isna().sum() == 0
+
+    # fill-in empty hypothesis
+    df = df.fillna("")
+
+    # encode labels
+    df["label"] = df["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2})
+
+    # 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"]),
+    })
+    ds[name] = Dataset.from_pandas(df, features=features)
+
+dataset = DatasetDict(ds)
+dataset.push_to_hub("fracas", token="<token>")
+```