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.gitattributes

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README.md

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----
-annotations_creators:
-- expert-generated
-language_creators:
-- crowdsourced
-language:
-- en
-license:
-- apache-2.0
-multilinguality:
-- monolingual
-pretty_name: 'wep-probes'
-size_categories:
-- 1K<n<10K
-source_datasets:
-- original
-task_categories:
-- multiple-choice
-- question-answering
-task_ids:
-- open-domain-qa
-- multiple-choice-qa
-- natural-language-inference
-tags:
-- wep
-- words of estimative probability
-- probability
-- logical reasoning
-- soft logic
----
-
-# Dataset accompanying the "Probing neural language models for understanding of words of estimative probability" article
-```bib
-@article{sileo2022probing,
-  title={Probing neural language models for understanding of words of estimative probability},
-  author={Sileo, Damien and Moens, Marie-Francine},
-  journal={arXiv preprint arXiv:2211.03358},
-  year={2022}
-}
-```

reasoning_1hop/wep-probes-test.parquet

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usnli/wep-probes-test.parquet

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wep-probes.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 = """\
-@article{sileo2022probing,
-  title={Probing neural language models for understanding of words of estimative probability},
-  author={Sileo, Damien and Moens, Marie-Francine},
-  journal={arXiv preprint arXiv:2211.03358},
-  year={2022}
-}
-"""
-
-_DESCRIPTION = """\
-Probing neural language models for understanding of words of estimative probability
-"""
-
-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)
-