Create wikigold.py

wikigold.py

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+import datasets
+from tqdm import tqdm
+
+_CITATION = """
+@inproceedings{balasuriya-etal-2009-named,
+    title = "Named Entity Recognition in Wikipedia",
+    author = "Balasuriya, Dominic  and
+      Ringland, Nicky  and
+      Nothman, Joel  and
+      Murphy, Tara  and
+      Curran, James R.",
+    booktitle = "Proceedings of the 2009 Workshop on The People{'}s Web Meets {NLP}: 
+    Collaboratively Constructed Semantic Resources (People{'}s Web)",
+    month = aug,
+    year = "2009",
+    address = "Suntec, Singapore",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/W09-3302",
+    pages = "10--18",
+}
+"""
+
+_LICENCE = "CC-BY 4.0"
+
+_DESCRIPTION = """
+WikiGold dataset.
+"""
+
+_URL = (
+    "https://github.com/juand-r/entity-recognition-datasets/raw/master/"
+    "data/wikigold/CONLL-format/data/wikigold.conll.txt"
+)
+
+# the label ids
+NER_TAGS_DICT = {
+    "O": 0,
+    "PER": 1,
+    "LOC": 2,
+    "ORG": 3,
+    "MISC": 4,
+}
+
+NER_BIO_TAGS_DICT = {
+    "O": 0,
+    "B-PER": 1,
+    "I-PER": 2,
+    "B-LOC": 3,
+    "I-LOC": 4,
+    "B-ORG": 5,
+    "I-ORG": 6,
+    "B-MISC": 7,
+    "I-MISC": 8
+}
+
+
+class WikiGoldConfig(datasets.BuilderConfig):
+    """BuilderConfig for WikiGold"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for WikiGold.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(WikiGoldConfig, self).__init__(**kwargs)
+
+
+class WikiGold(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.features.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.features.Sequence(
+                        datasets.features.ClassLabel(
+                            names=["O", "PER", "LOC", "ORG", "MISC"]
+                        )
+                    ),
+                    "ner_bio_tags": datasets.features.Sequence(
+                        datasets.features.ClassLabel(
+                            names=["O", "B-PER", "I-PER", "B-LOC", "I-LOC", 
+                            "B-ORG", "I-ORG", "B-MISC", "I-MISC"]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            citation=_CITATION,
+            license=_LICENCE,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": urls_to_download},
+            ),
+        ]
+
+    def _generate_examples(self, filepath=None):
+        num_lines = sum(1 for _ in open(filepath))
+        id = 0
+
+        with open(filepath, "r") as f:
+            tokens, ner_tags, ner_bio_tags = [], [], []
+            for line in tqdm(f, total=num_lines):
+                line = line.strip().split()
+
+                if line:
+                    assert len(line) == 2
+                    token, ner_tag = line
+
+                    if token == "-DOCSTART-":
+                        continue
+                    
+                    tokens.append(token)
+                    ner_bio_tags.append(ner_tag)
+                    if ner_tag != "O":
+                        ner_tag = ner_tag.split("-")[1]                  
+                    ner_tags.append(NER_TAGS_DICT[ner_tag])
+
+                elif tokens:
+                    # organize a record to be written into json
+                    record = {
+                        "tokens": tokens,
+                        "id": str(id),
+                        "ner_tags": ner_tags,
+                        "ner_bio_tags": ner_bio_tags,
+                    }
+                    tokens, ner_tags = [], []
+                    id += 1
+                    yield record["id"], record
+
+            # take the last sentence
+            if tokens:
+                record = {
+                    "tokens": tokens,
+                    "id": str(id),
+                    "ner_tags": ner_tags,
+                    "ner_bio_tags": ner_bio_tags,
+                }
+                yield record["id"], record