Update stan_large.py

stan_large.py

@@ -1,8 +1,8 @@
 """STAN large dataset"""
 
+from multiprocessing.sharedctypes import Value
 import datasets
 import pandas as pd
-import pickle5 as pickle
 
 _CITATION = """
 @inproceedings{maddela-etal-2019-multi,
@@ -77,22 +77,49 @@ class StanLarge(datasets.GeneratorBasedBuilder):
     def _generate_examples(self, filepath):
 
         def get_segmentation(row):
-            return row["goldtruths"][0]
-        
-        def get_alternatives(row):
-            segmentations = [{
-                "segmentation": x
-            } for x in row["goldtruths"]]
+            needle = row["hashtags"]
+            haystack = row["goldtruths"][0].strip()
+            output = ""
+            iterator = iter(haystack)
+            for char in needle:
+                output += char
+                while True:
+                    try:
+                        next_char = next(iterator)
+                        if next_char.lower() == char.lower():
+                            break
+                        elif next_char.isspace():
+                            output = output[0:-1] + next_char + output[-1]
+                    except StopIteration:
+                        break
+            return output
 
-            return segmentations[1:]
+        def get_alternatives(row, segmentation):
+            alts = list(set([x.strip() for x in row["goldtruths"]]))
+            alts = [x for x in alts if x != segmentation]
+            alts = [{"segmentation": x} for x in alts]
+            return alts
 
         with open(filepath, 'rb') as f:
-            records = pickle.load(f)
+            try:
+                import pickle
+                records = pickle.load(f)
+            except ValueError:
+                try:
+                    import pickle5 as pickle
+                    records = pickle.load(f)
+                except ModuleNotFoundError:
+                    raise ImportError(
+                        """To be able to use stan_large, you need to install the following dependencies['pickle5'] 
+                        using 'pip install pickle5' for instance"""
+                    )
         records = records.to_dict("records")
         for idx, row in enumerate(records):
+            segmentation = get_segmentation(row)
+            alternatives = get_alternatives(row, segmentation)
             yield idx, {
                 "index": idx,
                 "hashtag": row["hashtags"],
-                "segmentation": get_segmentation(row),
-                "alternatives": get_alternatives(row)
+                "segmentation": segmentation,
+                "alternatives": alternatives
             }