Upload cantemist-fasttext-8-ner.py with huggingface_hub

cantemist-fasttext-8-ner.py

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+# Loading script for the Cantemist NER dataset.
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@inproceedings{miranda2020named,
+  title={Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results},
+  author={Miranda-Escalada, A and Farr{\'e}, E and Krallinger, M},
+  booktitle={Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings},
+  year={2020}
+}"""
+
+_DESCRIPTION = """\
+https://temu.bsc.es/cantemist/
+"""
+
+_URL = "https://huggingface.co/datasets/Rodrigo1771/cantemist-fasttext-8-ner/resolve/main/"
+_TRAINING_FILE = "train.conll"
+_DEV_FILE = "dev.conll"
+_TEST_FILE = "test.conll"
+
+class CantemistNerConfig(datasets.BuilderConfig):
+    """BuilderConfig for Cantemist Ner dataset"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for CantemistNer.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(CantemistNerConfig, self).__init__(**kwargs)
+
+
+class CantemistNer(datasets.GeneratorBasedBuilder):
+    """Cantemist Ner dataset."""
+
+    BUILDER_CONFIGS = [
+        CantemistNerConfig(
+            name="CantemistNer",
+            version=datasets.Version("1.0.0"),
+            description="CantemistNer 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-MORFOLOGIA_NEOPLASIA",
+                                "I-MORFOLOGIA_NEOPLASIA",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://temu.bsc.es/cantemist/",
+            citation=_CITATION,
+        )
+
+    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 = []
+            ner_tags = []
+            for line in f:
+                if line.startswith("-DOCSTART-") or line == "" or line == "\n":
+                    if tokens:
+                        yield guid, {
+                            "id": str(guid),
+                            "tokens": tokens,
+                            "ner_tags": ner_tags,
+                        }
+                        guid += 1
+                        tokens = []
+                        ner_tags = []
+                else:
+                    # Cantemist tokens are tab separated
+                    splits = line.split("\t")
+                    tokens.append(splits[0])
+                    ner_tags.append(splits[-1].rstrip())
+            # last example
+            yield guid, {
+                "id": str(guid),
+                "tokens": tokens,
+                "ner_tags": ner_tags,
+            }