Create README.md

README.md

@@ -0,0 +1,89 @@
+## 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 = {}
+for i in path.rglob("<path to folder>/*.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
+```