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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 = """\ | |
| Probing neural language models for understanding of words of estimative probability | |
| Anonymous submission | |
| """ | |
| _DESCRIPTION = """\ | |
| Probing neural language models for understanding of words of estimative probability | |
| Anonymous submission | |
| """ | |
| URL = 'https://sileod.s3.eu-west-3.amazonaws.com/probability_words/' | |
| class WepProbeConfig(datasets.BuilderConfig): | |
| """BuilderConfig for WepProbe.""" | |
| def __init__( | |
| self, | |
| data_dir, | |
| label_classes=None, | |
| process_label=lambda x: x, | |
| **kwargs, | |
| ): | |
| super(WepProbeConfig, self).__init__(version=datasets.Version("1.0.5", ""), **kwargs) | |
| self.text_features = {k:k for k in ['context', 'hypothesis', 'valid_hypothesis', 'invalid_hypothesis','probability_word','distractor','hypothesis_assertion']} | |
| self.label_column = 'label' | |
| self.label_classes = ['valid', 'invalid'] | |
| self.data_url = URL | |
| self.url=URL | |
| self.data_dir=data_dir | |
| self.citation = _CITATION | |
| self.process_label = process_label | |
| class WepProbe(datasets.GeneratorBasedBuilder): | |
| """Evaluation of word estimative of probability understanding""" | |
| BUILDER_CONFIGS = [ | |
| WepProbeConfig( | |
| name="reasoning_1hop", | |
| data_dir="reasoning_1hop"), | |
| WepProbeConfig( | |
| name="reasoning_2hop", | |
| data_dir="reasoning_2hop"), | |
| WepProbeConfig( | |
| name="usnli", | |
| data_dir="usnli"), | |
| ] | |
| def _info(self): | |
| features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features.keys()} | |
| if self.config.label_classes: | |
| features["label"] = datasets.features.ClassLabel(names=self.config.label_classes) | |
| else: | |
| features["label"] = datasets.Value("float32") | |
| features["idx"] = datasets.Value("int32") | |
| features["probability"] = datasets.Value("float32") | |
| 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}_{split}.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).drop(['rnd','split','_'],axis=1,errors='ignore') | |
| df['idx']=df.index | |
| for idx, example in df.iterrows(): | |
| yield idx, dict(example) | |