Upload MORFITT.py

MORFITT.py

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+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,
+            }