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.gitattributes

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README.md

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----
-license: apache-2.0
----

medwiki/wikimedqa-test.parquet

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wikimedqa.py

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-# coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Lint as: python3
-
-
-import csv
-import os
-import textwrap
-import numpy as np
-import datasets
-import pandas as pd
-
-
-_CITATION = """\
-Anonymous submission
-"""
-
-_DESCRIPTION = """\
-Anonymous submission
-"""
-
-URL = 'https://sileod.s3.eu-west-3.amazonaws.com/wikimedqa/'
-
-
-class WikiMedQAConfig(datasets.BuilderConfig):
-    """BuilderConfig for WikiMedQA."""
-
-    def __init__(
-        self,
-        data_dir,
-        label_classes=None,
-        process_label=lambda x: x,
-        **kwargs,
-    ):
-
-        super(WikiMedQAConfig, self).__init__(version=datasets.Version("1.0.5", ""), **kwargs)
-        self.text_features = {k:k for k in ['text']+[f'option_{i}' for i in range(8)]}
-        self.label_column = 'label'
-        self.label_classes = list('01234567')
-        self.data_url = URL
-        self.url=URL
-        self.data_dir=data_dir
-        self.citation = _CITATION
-        self.process_label = process_label
-
-
-class WikiMedQA(datasets.GeneratorBasedBuilder):
-    """Evaluation of word estimative of probability understanding"""
-
-    BUILDER_CONFIGS = [
-        WikiMedQAConfig(
-            name="medwiki",
-            data_dir="medwiki"),
-         WikiMedQAConfig(
-            name="wikem",
-            data_dir="wikem"),
-        WikiMedQAConfig(
-            name="wikidoc",
-            data_dir="wikidoc"),
-        ]
-
-    def _info(self):
-        features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features.keys()}
-        features["label"] = datasets.features.ClassLabel(names=self.config.label_classes)
-        features["idx"] = datasets.Value("int32")
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(features),
-            homepage=self.config.url,
-            citation=self.config.citation + "\n" + _CITATION,
-        )
-    def _split_generators(self, dl_manager):
-        
-        data_dirs=[]
-        for split in ['train','validation','test']:
-            url=f'{URL}{self.config.data_dir}.csv'
-            print(url)
-            data_dirs+=[dl_manager.download(url)]
-        print(data_dirs)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "data_file": data_dirs[0],
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "data_file": data_dirs[1],
-                    "split": "dev",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "data_file": data_dirs[2],
-                    "split": "test",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, data_file, split):
-        df = pd.read_csv(data_file)
-        df=df[['text','options','label']]
-        train, dev, test = np.split(df.sample(frac=1, random_state=42), 
-                       [int(.9*len(df)), int(.95*len(df))])
-        df=eval(split)
-        df['options']=df['options'].map(eval)
-        for i in range(8):
-            df[f'option_{i}']=df.options.map(lambda x:x[i])
-        del df['options']
-        df['idx']=df.index
-        for idx, example in df.iterrows():
-            yield idx, dict(example)
-