Delete clinical_trials.py

clinical_trials.py

@@ -1,120 +0,0 @@
-'''
-Procesar así los datos en el terminal:
-
-import clinical_trials
-
-from clinical_trials import ClinicalTrials
-
-train_json = ClinicalTrials._generate_examples('train.json','train.conll')
-
-x = json.dumps([item for item in train_json])
-
-outFile = open("train.json",'w',encoding="utf8")
-print(x,file=outFile)
-outFile.close()
-
-'''
-
-
-import datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_LICENSE = "Creative Commons Attribution 4.0 International"
-
-_VERSION = "1.1.0"
-
-_URL = "https://huggingface.co/datasets/lcampillos/CT-EBM-ES"
-_TRAINING_FILE = "train.conll"
-_DEV_FILE = "dev.conll"
-_TEST_FILE = "test.conll"
-
-class ClinicalTrialsConfig(datasets.BuilderConfig):
-    """BuilderConfig for ClinicalTrials dataset."""
-
-    def __init__(self, **kwargs):
-        super(ClinicalTrialsConfig, self).__init__(**kwargs)
-
-
-class ClinicalTrials(datasets.GeneratorBasedBuilder):
-    """ClinicalTrials dataset."""
-
-    BUILDER_CONFIGS = [
-        ClinicalTrialsConfig(
-            name="ClinicalTrials", 
-            version=datasets.Version(_VERSION), 
-            description="ClinicalTrials 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-ANAT",
-                                "B-CHEM",
-                                "B-DISO",
-                                "B-PROC",
-                                "I-ANAT",
-                                "I-CHEM",
-                                "I-DISO",
-                                "I-PROC",
-                            ]
-                        )
-                    ),
-                }
-            ),
-            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 == "":
-                    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,
-            }
-