Upload hashset_distant_sampled.py

hashset_distant_sampled.py

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+"""HashSet dataset."""
+
+import datasets
+import pandas as pd
+
+_CITATION = """
+@article{kodali2022hashset,
+  title={HashSet--A Dataset For Hashtag Segmentation},
+  author={Kodali, Prashant and Bhatnagar, Akshala and Ahuja, Naman and Shrivastava, Manish and Kumaraguru, Ponnurangam},
+  journal={arXiv preprint arXiv:2201.06741},
+  year={2022}
+}
+"""
+
+_DESCRIPTION = """
+Hashset is a new dataset consisiting on 1.9k manually annotated and 3.3M loosely supervised tweets for testing the 
+efficiency of hashtag segmentation models. We compare State of The Art Hashtag Segmentation models on Hashset and other 
+baseline datasets (STAN and BOUN). We compare and analyse the results across the datasets to argue that HashSet can act 
+as a good benchmark for hashtag segmentation tasks.
+
+HashSet Distant: 3.3M loosely collected camel cased hashtags containing hashtag and their segmentation.
+
+HashSet Distant Sampled is a sample of 20,000 camel cased hashtags from the HashSet Distant dataset.
+"""
+_URL = "https://raw.githubusercontent.com/prashantkodali/HashSet/master/datasets/hashset/HashSet-Distant-sampled.csv"
+
+class HashSetDistantSampled(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")
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/prashantkodali/HashSet/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download(_URL)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files }),
+        ]
+
+    def _generate_examples(self, filepath):
+        records = pd.read_csv(filepath).to_dict("records")
+        for idx, row in enumerate(records):
+            yield idx, {
+                "index": row["Unnamed: 0.1"],
+                "hashtag": row["Unsegmented_hashtag"],
+                "segmentation": row["Segmented_hashtag"]
+            }