files uploaded

wl.py

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+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_LICENSE = "Creative Commons Attribution 4.0 International"
+
+_VERSION = "1.1.0"
+
+_URL = "https://huggingface.co/datasets/plncmm/wl/resolve/main/"
+_TRAINING_FILE = "train.conll"
+_DEV_FILE = "dev.conll"
+_TEST_FILE = "test.conll"
+
+class WLConfig(datasets.BuilderConfig):
+    """BuilderConfig for WL dataset."""
+
+    def __init__(self, **kwargs):
+        super(WLConfig, self).__init__(**kwargs)
+
+
+class WL(datasets.GeneratorBasedBuilder):
+    """WL dataset."""
+
+    BUILDER_CONFIGS = [
+        WLConfig(
+            name="WL", 
+            version=datasets.Version(_VERSION), 
+            description="WL dataset"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "B-Disease",
+                                "I-Disease",
+                                "B-Medication",
+                                "I-Medication",
+                                "B-Abbreviation",
+                                "I-Abbreviation",
+                                "B-Body_Part",
+                                "I-Body_Part",
+                                "B-Family_Member",
+                                "I-Family_Member",
+                                "B-Sign_or_Symptom",
+                                "I-Sign_or_Symptom",
+                                "B-Laboratory_or_Test_Result",
+                                "I-Laboratory_or_Test_Result",
+                                "B-Clinical_Finding",
+                                "I-Clinical_Finding",
+                                "B-Diagnostic_Procedure",
+                                "I-Diagnostic_Procedure",
+                                "B-Laboratory_Procedure",
+                                "I-Laboratory_Procedure",
+                                "B-Therapeutic_Procedure",
+                                "I-Therapeutic_Procedure",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{_URL}{_TRAINING_FILE}",
+            "dev":   f"{_URL}{_DEV_FILE}",
+            "test":  f"{_URL}{_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["dev"]}),
+            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 = []
+            pos_tags = []
+            ner_tags = []
+            for line in f:
+                if line == "\n":
+                    if tokens:
+                        yield guid, {
+                            "id": str(guid),
+                            "tokens": tokens,
+                            "ner_tags": ner_tags,
+                        }
+                        guid += 1
+                        tokens = []
+                        ner_tags = []
+                else:
+                    splits = line.split(" ")
+                    tokens.append(splits[0])
+                    ner_tags.append(splits[-1].rstrip())
+            # last example
+            yield guid, {
+                "id": str(guid),
+                "tokens": tokens,
+                "ner_tags": ner_tags,
+            }