Delete loading script

MORFITT.py

@@ -1,106 +0,0 @@
-import os
-import json
-import random
-
-import datasets
-import numpy as np
-import pandas as pd
-
-_CITATION = """\
-ddd
-"""
-
-_DESCRIPTION = """\
-ddd
-"""
-
-_HOMEPAGE = "ddd"
-
-_URL = "https://huggingface.co/datasets/Dr-BERT/MORFITT/resolve/main/data.zip"
-
-_LICENSE = "unknown"
-
-_SPECIALITIES = ['microbiology', 'etiology', 'virology', 'physiology', 'immunology', 'parasitology', 'genetics', 'chemistry', 'veterinary', 'surgery', 'pharmacology', 'psychology']
-
-class MORFITT(datasets.GeneratorBasedBuilder):
-
-    DEFAULT_CONFIG_NAME = "source"
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="source", version="1.0.0", description="The MORFITT corpora"),
-    ]
-
-    def _info(self):
-
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "abstract": datasets.Value("string"),
-                "specialities": datasets.Sequence(
-                    datasets.features.ClassLabel(names=_SPECIALITIES),
-                ),
-                "specialities_one_hot": datasets.Sequence(
-                    datasets.Value("float"),
-                ),
-            }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=str(_LICENSE),
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-
-        data_dir = dl_manager.download_and_extract(_URL).rstrip("/")
-            
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "tsv_file": data_dir + "/train.tsv",
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "tsv_file": data_dir + "/dev.tsv",
-                    "split": "validation",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "tsv_file": data_dir + "/test.tsv",
-                    "split": "test",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, tsv_file, split):
-
-        # Load TSV file
-        df = pd.read_csv(tsv_file, sep="\t")
-
-        for index, e in df.iterrows():
-
-            specialities = e["specialities"].split("|")
-
-            # Empty one hot vector
-            one_hot = [0.0 for i in _SPECIALITIES]
-
-            # Fill up the one hot vector
-            for s in specialities:
-                one_hot[_SPECIALITIES.index(s)] = 1.0
-
-            yield e["identifier"], {
-                "id": e["identifier"],
-                "abstract": e["abstract"],
-                "specialities": specialities,
-                "specialities_one_hot": one_hot,
-            }