Update README.md

README.md

@@ -8,9 +8,10 @@ where the premise differs from the hypothesis by conjuncts removed, added, or re
 
 ## Dataset curation
 No curation is performed. This dataset is "as-is". The label mapping is the usual `{"entailment": 0, "neutral": 1, "contradiction": 2}`
-used in NLI datasets.
+used in NLI datasets. Note that labels for `test` split are not available. 
+Also, the `train` split is originally named `adversarial_train_15k`.
 
-NOTE: labels for `test` split are not available.
+Note that there are 2 instances (join on "premise", "hypothesis", "label") present both in `train` and `dev`.
 
 
 ## Code to create the dataset
@@ -25,31 +26,46 @@ paths = {
     "test": "<path_to_folder>/ConjNLI-master/data/NLI/conj_test.tsv",
 }
 
-if __name__ =="__main__":
-    dataset_splits = {}    
-    for split, path in paths.items():
-        
-        # read dataset split
-        df = pd.read_csv(paths[split], sep="\t")
-    
-        # encode labels using the default mapping used by other nli datasets
-        # i.e, entailment: 0, neutral: 1, contradiction: 2
-        df.columns = df.columns.str.lower()
-        if not "test" in path:
-            df["label"] = df["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2})
-        else:
-            df["label"] = -1
-    
-        # cast to dataset
-        features = Features({
-            "premise": Value(dtype="string", id=None),
-            "hypothesis": Value(dtype="string", id=None),
-            "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
-        })
-        dataset = Dataset.from_pandas(df, features=features)
-        dataset_splits[split] = dataset
-    
-    conj_nli = DatasetDict(dataset_splits)
-    conj_nli.push_to_hub("pietrolesci/conj_nli", token="<token>")
+dataset_splits = {}
+for split, path in paths.items():
 
+    # load data
+    df = pd.read_csv(paths[split], sep="\t")
+
+    # encode labels using the default mapping used by other nli datasets
+    # i.e, entailment: 0, neutral: 1, contradiction: 2
+    df.columns = df.columns.str.lower()
+    if not "test" in path:
+        df["label"] = df["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2})
+
+    else:
+        df["label"] = -1
+
+    # cast to dataset
+    features = Features({
+        "premise": Value(dtype="string", id=None),
+        "hypothesis": Value(dtype="string", id=None),
+        "label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
+    })
+    dataset = Dataset.from_pandas(df, features=features)
+    dataset_splits[split] = dataset
+
+conj_nli = DatasetDict(dataset_splits)
+conj_nli.push_to_hub("pietrolesci/conj_nli", token="<token>")
+
+
+# check overlap between splits
+from itertools import combinations
+for i, j in combinations(conj_nli.keys(), 2):
+    print(
+        f"{i} - {j}: ",
+        pd.merge(
+            conj_nli[i].to_pandas(), 
+            conj_nli[j].to_pandas(), 
+            on=["premise", "hypothesis", "label"], how="inner"
+        ).shape[0],
+    )
+#> train - dev:  2
+#> train - test:  0
+#> dev - test:  0
 ```