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Create README.md

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