EBIQUITY V2 Stemmed dataset added

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml

README.md

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+---
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: tokens
+    sequence: string
+  - name: ner_tags
+    sequence:
+      class_label:
+        names:
+          '0': O
+          '1': B-PER
+          '2': I-PER
+          '3': B-ORG
+          '4': I-ORG
+          '5': B-LOC
+          '6': I-LOC
+          '7': B-MISC
+          '8': I-MISC
+  config_name: ebiquity-v2-stemmed
+  splits:
+  - name: train
+    num_bytes: 2192488
+    num_examples: 3289
+  download_size: 1414009
+  dataset_size: 2192488
+---

ebiquity-v2-stemmed.py

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+import re
+
+import datasets
+
+_CITATION = """
+@INPROCEEDINGS{
+  8998477,
+  author={O. M. {Singh} and A. {Padia} and A. {Joshi}},
+  booktitle={2019 IEEE 5th International Conference on Collaboration and Internet Computing (CIC)},
+  title={Named Entity Recognition for Nepali Language},
+  year={2019},
+  volume={},
+  number={},
+  pages={184-190},
+  keywords={Named Entity Recognition;Nepali;Low-resource;BiLSTM;CNN;Grapheme},
+  doi={10.1109/CIC48465.2019.00031},
+  ISSN={null},
+  month={Dec},}
+"""
+
+_DESCRIPTION = """
+Ebiquity V2 (stemmed) dataset for Nepali NER task. The dataset is tagged with BIO scheme.
+"""
+
+_URL = "https://raw.githubusercontent.com/mani-rai/nepali-ner/master/data/ebiquity_v2/stemmed/total.bio"
+
+
+class EbiquityV2StemmedConfig(datasets.BuilderConfig):
+    """BuilderConfig for Ebiquity V2 Stemmed"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for Ebiquity V2 Stemmed.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(EbiquityV2StemmedConfig, self).__init__(**kwargs)
+
+
+class EbiquityV2Stemmed(datasets.GeneratorBasedBuilder):
+    """Ebiquity V2 Stemmed"""
+
+    BUILDER_CONFIGS = [
+        EbiquityV2StemmedConfig(name="ebiquity-v2-stemmed", version=datasets.Version("1.0.0"), description="Ebiquity v2 stemmed dataset"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "B-PER",
+                                "I-PER",
+                                "B-ORG",
+                                "I-ORG",
+                                "B-LOC",
+                                "I-LOC",
+                                "B-MISC",
+                                "I-MISC",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://arxiv.org/abs/1908.05828",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_file = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file})
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            guid = 0
+            tokens = []
+            ner_tags = []
+            for line in f:
+                if line == "" or line == "\n":
+                    if tokens:
+                        yield guid, {
+                            "id": str(guid),
+                            "tokens": tokens,
+                            "ner_tags": ner_tags,
+                        }
+                        guid += 1
+                        tokens = []
+                        ner_tags = []
+                else:
+                    splits = re.split('\t+', line)
+                    tokens.append(splits[0])
+                    ner_tags.append(splits[1].rstrip())
+            yield guid, {
+                "id": str(guid),
+                "tokens": tokens,
+                "ner_tags": ner_tags,
+            }