pietrolesci/joci

Overview

Original dataset available here. 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, 2)

{
    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

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>")