Update indian_names.py

indian_names.py

@@ -1,28 +1,91 @@
+# coding=utf-8
+# Copyright 2020 HuggingFace Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""The WNUT 17 Emerging Entities Dataset."""
+
+
 import datasets
 
 
 logger = datasets.logging.get_logger(__name__)
 
 
-csv_file_path = "https://github.com/Kriyansparsana/demorepo/blob/main/wnut17train.conll"
+_CITATION = """\
+@inproceedings{derczynski-etal-2017-results,
+    title = "Results of the {WNUT}2017 Shared Task on Novel and Emerging Entity Recognition",
+    author = "Derczynski, Leon  and
+      Nichols, Eric  and
+      van Erp, Marieke  and
+      Limsopatham, Nut",
+    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
+    month = sep,
+    year = "2017",
+    address = "Copenhagen, Denmark",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/W17-4418",
+    doi = "10.18653/v1/W17-4418",
+    pages = "140--147",
+    abstract = "This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions.
+                Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization),
+                but recall on them is a real problem in noisy text - even among annotators.
+                This drop tends to be due to novel entities and surface forms.
+                Take for example the tweet {``}so.. kktny in 30 mins?!{''} {--} even human experts find the entity {`}kktny{'}
+                hard to detect and resolve. The goal of this task is to provide a definition of emerging and of rare entities,
+                and based on that, also datasets for detecting these entities. The task as described in this paper evaluated the
+                ability of participating entries to detect and classify novel and emerging named entities in noisy text.",
+}
+"""
 
+_DESCRIPTION = """\
+WNUT 17: Emerging and Rare entity recognition
+This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions.
+Named entities form the basis of many modern approaches to other tasks (like event clustering and summarisation),
+but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms.
+Take for example the tweet “so.. kktny in 30 mins?” - even human experts find entity kktny hard to detect and resolve.
+This task will evaluate the ability to detect and classify novel, emerging, singleton named entities in noisy text.
+The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities.
+"""
 
-class indina_namesConfig(datasets.BuilderConfig):
+_URL = "https://github.com/Kriyansparsana/demorepo/blob/main/"
+_TRAINING_FILE = "wnut17train.conll"
+
+
+class indian_namesConfig(datasets.BuilderConfig):
+    """The WNUT 17 Emerging Entities Dataset."""
 
     def __init__(self, **kwargs):
-        super(indina_namesConfig, self).__init__(**kwargs)
+        """BuilderConfig for WNUT 17.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(indian_namesConfig, self).__init__(**kwargs)
 
 
-class indina_names(datasets.GeneratorBasedBuilder):
+class indian_names(datasets.GeneratorBasedBuilder):
+    """The WNUT 17 Emerging Entities Dataset."""
 
     BUILDER_CONFIGS = [
-        indina_namesConfig(
-            name="indina_names", version=datasets.Version("1.0.0"), description="The indina_names Emerging Entities Dataset"
+        indian_namesConfig(
+            name="indian_names", version=datasets.Version("1.0.0"), description="The indian_names Emerging Entities Dataset"
         ),
     ]
 
     def _info(self):
         return datasets.DatasetInfo(
+            description=_DESCRIPTION,
             features=datasets.Features(
                 {
                     "id": datasets.Value("string"),
@@ -30,25 +93,40 @@ class indina_names(datasets.GeneratorBasedBuilder):
                     "ner_tags": datasets.Sequence(
                         datasets.features.ClassLabel(
                             names=[
-                                "B-PER",
-                                "B-ORG",
+                                "O",
+                                "B-corporation",
+                                "I-corporation",
+                                "B-creative-work",
+                                "I-creative-work",
+                                "B-group",
+                                "I-group",
+                                "B-location",
+                                "I-location",
+                                "B-person",
+                                "I-person",
+                                "B-product",
+                                "I-product",
                             ]
                         )
                     ),
                 }
             ),
             supervised_keys=None,
+            homepage="http://noisy-text.github.io/2017/emerging-rare-entities.html",
+            citation=_CITATION,
         )
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
         urls_to_download = {
-            "train": f"{csv_file_path}",
+            "train": f"{_URL}{_TRAINING_FILE}",
+            
         }
         downloaded_files = dl_manager.download_and_extract(urls_to_download)
 
         return [
             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            
         ]
 
     def _generate_examples(self, filepath):
@@ -61,9 +139,9 @@ class indina_names(datasets.GeneratorBasedBuilder):
                 row = row.rstrip()
                 if row:
                     token, label = row.split("\t")
-                    row_values = row.split("\t")
                     current_tokens.append(token)
                     current_labels.append(label)
+                else:
                     # New sentence
                     if not current_tokens:
                         # Consecutive empty lines will cause empty sentences
@@ -87,4 +165,4 @@ class indina_names(datasets.GeneratorBasedBuilder):
                     "id": str(sentence_counter),
                     "tokens": current_tokens,
                     "ner_tags": current_labels,
-                }
+                }