Update parquet files

.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
-*.h5 filter=lfs diff=lfs merge=lfs -text
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-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.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
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-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
-*.aac filter=lfs diff=lfs merge=lfs -text
-*.flac filter=lfs diff=lfs merge=lfs -text
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README.md

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-## Overview
-
-Original dataset page [here](https://abhilasharavichander.github.io/NLI_StressTest/) and dataset available [here](https://drive.google.com/open?id=1faGA5pHdu5Co8rFhnXn-6jbBYC2R1dhw).
-
-
-## Dataset curation
-Added new column `label` with encoded labels with the following mapping
-
-```
-{"entailment": 0, "neutral": 1, "contradiction": 2}
-```
-
-and the columns with parse information are dropped as they are not well formatted.
-
-Also, the name of the file from which each instance comes is added in the column `dtype`.
-
-
-## Code to create the dataset
-
-```python
-import pandas as pd
-from datasets import Dataset, ClassLabel, Value, Features, DatasetDict
-import json
-from pathlib import Path
-
-
-# load data
-ds = {}
-path = Path("<path to folder>")
-for i in path.rglob("*.jsonl"): 
-    print(i)
-    name = str(i).split("/")[0].lower()
-    dtype = str(i).split("/")[1].lower()
-    
-    # read data
-    with i.open("r") as fl:
-        df = pd.DataFrame([json.loads(line) for line in fl])
-    
-    # select columns
-    df = df.loc[:, ["sentence1", "sentence2", "gold_label"]]
-    
-    # add file name as column
-    df["dtype"] = dtype
-    
-    # encode labels
-    df["label"] = df["gold_label"].map({"entailment": 0, "neutral": 1, "contradiction": 2})
-    ds[name] = df
-        
-# cast to dataset
-features = Features(
-    {
-        "sentence1": Value(dtype="string"),
-        "sentence2": Value(dtype="string"),
-        "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
-        "dtype": Value(dtype="string"),
-        "gold_label": Value(dtype="string"),
-    }
-)
-ds = DatasetDict({k: Dataset.from_pandas(v, features=features) for k, v in ds.items()})
-ds.push_to_hub("pietrolesci/stress_tests_nli", 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=["sentence1", "sentence2", "label"], 
-            how="inner",
-        ).shape[0],
-    )
-#> numerical_reasoning - negation:  0
-#> numerical_reasoning - length_mismatch:  0
-#> numerical_reasoning - spelling_error:  0
-#> numerical_reasoning - word_overlap:  0
-#> numerical_reasoning - antonym:  0
-#> negation - length_mismatch:  0
-#> negation - spelling_error:  0
-#> negation - word_overlap:  0
-#> negation - antonym:  0
-#> length_mismatch - spelling_error:  0
-#> length_mismatch - word_overlap:  0
-#> length_mismatch - antonym:  0
-#> spelling_error - word_overlap:  0
-#> spelling_error - antonym:  0
-#> word_overlap - antonym:  0
-```

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