Upload stan_small.py

stan_small.py

@@ -0,0 +1,76 @@
+"""STAN small dataset by Bansal et al.."""
+
+import datasets
+import pandas as pd
+import pickle
+
+_CITATION = """
+@misc{bansal2015deep,
+      title={Towards Deep Semantic Analysis Of Hashtags}, 
+      author={Piyush Bansal and Romil Bansal and Vasudeva Varma},
+      year={2015},
+      eprint={1501.03210},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR}
+}
+"""
+
+_DESCRIPTION = """
+Manually Annotated Stanford Sentiment Analysis Dataset by Bansal et al..
+"""
+_URLS = {
+    "test": "https://github.com/prashantkodali/HashSet/raw/master/datasets/stan-small-bansal_et_al.pkl"
+}
+
+class StanSmall(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "index": datasets.Value("int32"),
+                    "hashtag": datasets.Value("string"),
+                    "segmentation": datasets.Value("string"),
+                    "alternatives": datasets.Sequence(
+                        {
+                            "segmentation": datasets.Value("string")
+                        }
+                    )
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/mounicam/hashtag_master",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download(_URLS)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"] }),
+        ]
+
+    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"]]
+
+            return segmentations[1:]
+
+        with open(filepath, 'rb') as f:
+            records = pickle.load(f)
+        records = records.to_dict("records")
+        for idx, row in enumerate(records):
+            yield idx, {
+                "index": idx,
+                "hashtag": row["hashtags"],
+                "segmentation": get_segmentation(row),
+                "alternatives": get_alternatives(row)
+            }