Create README.md

README.md

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+## Overview
+
+Original dataset available [here](https://github.com/sheng-z/JOCI/tree/master/data). 
+This dataset is the "full" JOCI dataset, which is the file named `joci.csv.zip`.
+
+
+# Dataset curation
+The following processing is applied,
+
+- `label` column renamed to `original_label`
+- creation of the `label` column using the following mapping, using common practices ([1](https://github.com/rabeehk/robust-nli/blob/c32ff958d4df68ac2fad9bf990f70d30eab9f297/data/scripts/joci.py#L22-L27), [2](https://github.com/azpoliak/hypothesis-only-NLI/blob/b045230437b5ba74b9928ca2bac5e21ae57876b9/data/convert_joci.py#L7-L12))
+
+```
+{
+    0: "contradiction", 
+    1: "contradiction",  
+    2: "neutral",
+    3: "neutral",
+    4: "neutral",
+    5: "entailment",
+}
+```
+
+- finally, converting this to the usual NLI classes, that is `{"entailment": 0, "neutral": 1, "contradiction": 2}`
+
+
+## Code to create dataset
+```python
+import pandas as pd
+from datasets import Features, Value, ClassLabel, Dataset
+
+
+# read data
+df = pd.read_csv("<path to folder>/joci.csv")
+
+# column name to lower
+df.columns = df.columns.str.lower()
+
+# rename label column
+df = df.rename(columns={"label": "original_label"})
+
+# encode labels
+df["label"] = df["original_label"].map({
+    0: "contradiction", 
+    1: "contradiction",  
+    2: "neutral",
+    3: "neutral",
+    4: "neutral",
+    5: "entailment",
+})
+
+# encode labels
+df["label"] = df["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2})
+
+# cast to dataset
+features = Features({
+    "context": Value(dtype="string"),
+    "hypothesis": Value(dtype="string"),
+    "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
+    "original_label": Value(dtype="int32"),
+    "context_from": Value(dtype="string"),
+    "hypothesis_from": Value(dtype="string"),
+    "subset": Value(dtype="string"),
+})
+ds = Dataset.from_pandas(df, features=features)
+ds.push_to_hub("joci", token="<token>")
+```