Create README.md

README.md

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+## Overview
+
+Original dataset [here](https://github.com/aylai/MultiPremiseEntailment).
+
+
+## Dataset curation
+Same data and splits as the original. The following columns have been added:
+
+- `premise`: concatenation of `premise1`, `premise2`, `premise3`, and `premise4`
+- `label`: encoded `gold_label` with the following mapping `{"entailment": 0, "neutral": 1, "contradiction": 2}`
+
+
+## Code to create the dataset
+
+```python
+import pandas as pd
+from datasets import Features, Value, ClassLabel, Dataset, DatasetDict
+from pathlib import Path
+
+
+# read data
+path = Path(".")
+datasets = {}
+for dataset_path in path.rglob("*.txt"):
+    df = pd.read_csv(dataset_path, sep="\t")
+    datasets[dataset_path.name.split("_")[1].split(".")[0]] = df
+    
+
+ds = {}
+for name, df_ in datasets.items():
+    df = df_.copy()
+    
+    # fix parsing error for dev split
+    if name == "dev":
+        # fix parsing error
+        df.loc[df["contradiction_judgments"] == "3   contradiction", "contradiction_judgments"] = 3
+        df.loc[df["gold_label"].isna(), "gold_label"] = "contradiction"
+    
+    # check no nan
+    assert df.isna().sum().sum() == 0
+
+    # fix dtypes
+    for col in ("entailment_judgments", "neutral_judgments", "contradiction_judgments"):
+        df[col] = df[col].astype(int)
+    
+    # fix premise column
+    for i in range(1, 4 + 1):
+        df[f"premise{i}"] = df[f"premise{i}"].str.split("/", expand=True)[1]
+    df["premise"] = df[[f"premise{i}" for i in range(1, 4 + 1)]].agg(" ".join, axis=1)
+
+    # encode labels
+    df["label"] = df["gold_label"].map({"entailment": 0, "neutral": 1, "contradiction": 2})
+
+    # cast to dataset
+    features = Features({
+        "premise1": Value(dtype="string", id=None),
+        "premise2": Value(dtype="string", id=None),
+        "premise3": Value(dtype="string", id=None),
+        "premise4": Value(dtype="string", id=None),
+        "premise": Value(dtype="string", id=None),
+        "hypothesis": Value(dtype="string", id=None),
+        "entailment_judgments": Value(dtype="int32"),
+        "neutral_judgments": Value(dtype="int32"),
+        "contradiction_judgments": Value(dtype="int32"),
+        "gold_label": Value(dtype="string"),
+        "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
+    })
+
+    ds[name] = Dataset.from_pandas(df, features=features)
+    
+# push to hub
+ds = DatasetDict(ds)
+ds.push_to_hub("mpe", token="<token>")
+```