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.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bin.* filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
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-*.model filter=lfs diff=lfs merge=lfs -text
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-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
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-*.pt filter=lfs diff=lfs merge=lfs -text
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-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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-*tfevents* filter=lfs diff=lfs merge=lfs -text
-# Audio files - uncompressed
-*.pcm filter=lfs diff=lfs merge=lfs -text
-*.sam filter=lfs diff=lfs merge=lfs -text
-*.raw filter=lfs diff=lfs merge=lfs -text
-# Audio files - compressed
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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("<path to files>")
-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>")
-
-# check overlap between splits
-from itertools import combinations
-for i, j in combinations(ds.keys(), 2):
-    print(
-        f"{i} - {j}: ",
-        pd.merge(
-            ds[i].to_pandas(), 
-            ds[j].to_pandas(), 
-            on=["premise", "hypothesis", "label"], 
-            how="inner",
-        ).shape[0],
-    )
-#> dev - test:  0
-#> dev - train:  0
-#> test - train:  0
-```

dataset_infos.json

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