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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>")
```
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