Upload ner.py

ner.py

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+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """
+@InProceedings{huggingface:dataset,
+title = {Luganda, Kanuri, and Hausa NER Dataset},
+author = {multiple authors},
+year = {2022}
+}
+"""
+
+_DESCRIPTION = """
+LugandaPII is a dataset that includes named entities such as PERSON, ORG, LOCATION, NORP, USERID, and DATE. 
+The dataset is available in Lum, Kanuri, and Hausa languages, distributed across train, validation, and test splits.
+"""
+
+# Data directory structure
+data_directory = """
+data
+├── hau
+│   ├── test.txt
+│   ├── train.txt
+│   └── val.txt
+├── knr
+│   ├── test.txt
+│   ├── train.txt
+│   └── val.txt
+├── lug
+│   ├── test.txt
+│   ├── train.txt
+│   └── val.txt
+└── lum
+    ├── test.txt
+    ├── train.txt
+    └── val.txt
+"""
+
+_URL = "https://github.com/EricPeter/pii/raw/main/data"
+_TRAINING_FILE = "train.txt"
+_VAL_FILE = "val.txt"
+_TEST_FILE = "test.txt"
+
+class LugPIIConfig(datasets.BuilderConfig):
+    """Configuration for LugandaPII dataset."""
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+class Masakhaner(datasets.GeneratorBasedBuilder):
+    """Generator for Masakhaner dataset."""
+    BUILDER_CONFIGS = [
+        LugPIIConfig(name="lug", version=datasets.Version("1.0.0"), description="PII NER dataset for Luganda."),
+        LugPIIConfig(name="hau", version=datasets.Version("1.0.0"), description="PII NER dataset for Hausa."),
+        LugPIIConfig(name="knr", version=datasets.Version("1.0.0"), description="PII NER dataset for Hausa."),
+        LugPIIConfig(name="lum", version=datasets.Version("1.0.0"), description="PII NER dataset for Hausa."),
+
+    ]
+
+    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=['B-DATE',
+                    'B-GOVT_ID',
+                    'B-LOC',
+                    'B-LOCATION',
+                    'B-NORP',
+                    'B-ORG',
+                    'B-PERSON',
+                    'B-USERID',
+                    'B-USER_ID',
+                    'I-DATE',
+                    'I-GOVT_ID',
+                    'I-LOC',
+                    'I-LOCATION',
+                    'I-NORP',
+                    'I-ORG',
+                    'I-PERSON',
+                    'I-USERID',
+                    'I-USER_ID',
+                    'L-DATE',
+                    'L-GOVT_ID',
+                    'L-LOC',
+                    'L-LOCATION',
+                    'L-NORP',
+                    'L-ORG',
+                    'L-PERSON',
+                    'L-USERID',
+                    'L-USER_ID',
+                    'O',
+                    'U-DATE',
+                    'U-GOVT_ID',
+                    'U-LOCATION',
+                    'U-NORP',
+                    'U-ORG',
+                    'U-PERSON',
+                    'U-USERID'])),
+            }),
+            supervised_keys=None,
+            homepage="",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        lang_code = self.config.name  # 'lug', 'hau', etc.
+        urls_to_download = {
+            "train": f"{_URL}/{lang_code}/{_TRAINING_FILE}",
+            "val": f"{_URL}/{lang_code}/{_VAL_FILE}",
+            "test": f"{_URL}/{lang_code}/{_TEST_FILE}"
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+        
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["val"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]})
+        ]
+
+
+    def _generate_examples(self, filepath):
+        logger.info("Generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            guid = 0
+            tokens = []
+            ner_tags = []
+            for line in f:
+                if line.strip() == "":
+                    if tokens:
+                        yield guid, {"id": str(guid), "tokens": tokens, "ner_tags": ner_tags}
+                        guid += 1
+                        tokens = []
+                        ner_tags = []
+                    continue
+                splits = line.strip().split()
+                tokens.append(splits[0])
+                ner_tags.append(splits[1])
+            if tokens:
+                yield guid, {"id": str(guid), "tokens": tokens, "ner_tags": ner_tags}