Upload hashset_manual.py

hashset_manual.py

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+"""HashSet dataset."""
+
+import datasets
+import pandas as pd
+import json
+
+_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 Manual: contains 1.9k manually annotated hashtags. Each row consists of the hashtag, segmented 
+hashtag ,named entity annotations, a list storing whether the hashtag contains mix of hindi and english 
+tokens and/or contains non-english tokens.
+"""
+_URL = "https://raw.githubusercontent.com/prashantkodali/HashSet/master/datasets/hashset/HashSet-Manual.csv"
+
+class HashSetManual(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"),
+                    "spans": datasets.Sequence(
+                        {
+                            "start": datasets.Value("int32"),
+                            "end": datasets.Value("int32"),
+                            "text": datasets.Value("string")
+                        }
+                    ),
+                    "source": datasets.Value("string"),
+                    "gold_position": datasets.Value("int32"),
+                    "english": datasets.Value("bool"),
+                    "hindi": datasets.Value("bool"),
+                    "annotator_id": datasets.Value("int32"),
+                    "annotation_id": datasets.Value("int32"),
+                    "created_at": datasets.Value("timestamp[us]"),
+                    "updated_at": datasets.Value("timestamp[us]"),
+                    "lead_time": datasets.Value("float64"),
+                    "rank": datasets.Sequence(
+                        {
+                            "position": datasets.Value("int32"),
+                            "candidate": 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):
+
+        def read_language_labels(field):
+            mix_label = "Hashtag has a mix of english and hindi tokens"
+            hindi_label = "Hashtag has non english token "
+            try:
+                record = json.loads(field)
+            except json.decoder.JSONDecodeError:
+                record = {"choices": [field]}
+            
+            if mix_label in record["choices"]:
+                english = True
+                hindi = True
+            elif hindi_label in record["choices"]:
+                english = False
+                hindi = True
+            else:
+                english = True
+                hindi = False
+            return english, hindi
+
+        def read_entities(field):
+            try:
+                record = json.loads(field)
+            except json.decoder.JSONDecodeError:
+                return []
+            output = []
+            for row in record:
+                output.append({
+                    "start": row.get("start", None),
+                    "end": row.get("end", None),
+                    "text": row.get("text", None)
+                })
+            return output
+
+        def read_rank(row):
+            output = []
+            for i in range(10):
+                output.append({
+                    "position": str(i+1),
+                    "candidate": row[str(i+1)]
+                })
+            return output
+
+        def get_gold_position(field):
+            output = field.strip("$")
+            try:
+                return int(output)
+            except ValueError:
+                return None
+
+        records = pd.read_csv(filepath).to_dict("records")
+        for idx, row in enumerate(records):
+            english, hindi = read_language_labels(row["mutlitoken"])
+            yield idx, {
+                    "index": row["Unnamed: 0"],
+                    "hashtag": row["Hashtag"],
+                    "segmentation": row["Final Segmentation"],
+                    "spans": read_entities(row["charner"]),
+                    "source": row["Source"],
+                    "gold_position": get_gold_position(row["topk"]),
+                    "english": english,
+                    "hindi": hindi,
+                    "annotator_id": int(row["annotator"]),
+                    "annotation_id": int(row["annotation_id"]),
+                    "created_at": row["created_at"],
+                    "updated_at": row["updated_at"],
+                    "lead_time": row["lead_time"],
+                    "rank": read_rank(row)
+                }