Create README.md

README.md

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+## Overview
+Original dataset is available on the HuggingFace Hub [here](https://huggingface.co/datasets/scitail).
+
+
+## Dataset curation
+This is the same as the `snli_format` split of the SciTail dataset available on the HuggingFace Hub (i.e., same data, same splits, etc).
+The only differences are the following:
+
+- selecting only the columns `["sentence1", "sentence2", "gold_label"]`
+- renaming columns with the following mapping `{"sentence1": "premise", "sentence2": "hypothesis", "gold_label": "label"}`
+- encoding labels with the following mapping `{"entailment": 0, "neutral": 1, "contradiction": 2}`
+
+
+## Code to create the dataset
+```python
+from datasets import Features, Value, ClassLabel, Dataset, DatasetDict, load_dataset
+
+# load datasets from the Hub
+dd = load_dataset("scitail", "snli_format")
+
+ds = {}
+for name, df_ in dd.items():
+    df = df_.to_pandas()
+
+    # select important columns
+    df = df[["sentence1", "sentence2", "gold_label"]]
+
+    # rename columns
+    df = df.rename(columns={"sentence1": "premise", "sentence2": "hypothesis", "gold_label": "label"})
+
+    # 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("scitail", token="<token>")
+```