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linguisticprobing

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Remove legacy dataset loading script

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  1. linguisticprobing.py +0 -1248
linguisticprobing.py DELETED
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- # coding=utf-8
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- # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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- #
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- # Licensed under the Apache License, Version 2.0 (the "License");
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- # you may not use this file except in compliance with the License.
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- # You may obtain a copy of the License at
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- #
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- # http://www.apache.org/licenses/LICENSE-2.0
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- #
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- # Unless required by applicable law or agreed to in writing, software
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- # distributed under the License is distributed on an "AS IS" BASIS,
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- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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- # See the License for the specific language governing permissions and
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- # limitations under the License.
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-
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- # Lint as: python3
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- """Linguistic Probing Benchmark from SentEval"""
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-
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- from __future__ import absolute_import, division, print_function
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-
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- import csv
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- import os
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- import textwrap
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-
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- import six
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-
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- import datasets
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-
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-
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- _Linguisticprobing_CITATION = r"""@inproceedings{conneau-etal-2018-cram,
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- title = "What you can cram into a single {\$}{\&}!{\#}* vector: Probing sentence embeddings for linguistic properties",
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- author = {Conneau, Alexis and
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- Kruszewski, German and
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- Lample, Guillaume and
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- Barrault, Lo{\"\i}c and
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- Baroni, Marco},
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- booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
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- month = jul,
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- year = "2018",
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- address = "Melbourne, Australia",
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- publisher = "Association for Computational Linguistics",
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- url = "https://www.aclweb.org/anthology/P18-1198",
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- doi = "10.18653/v1/P18-1198",
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- pages = "2126--2136",
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- abstract = "Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing. {``}Downstream{''} tasks, often based on sentence classification, are commonly used to evaluate the quality of sentence representations. The complexity of the tasks makes it however difficult to infer what kind of information is present in the representations. We introduce here 10 probing tasks designed to capture simple linguistic features of sentences, and we use them to study embeddings generated by three different encoders trained in eight distinct ways, uncovering intriguing properties of both encoders and training methods.",
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- }
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- """
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-
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- _Linguisticprobing_DESCRIPTION = """\
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- 10 probing tasks designed to capture simple linguistic features of sentences,
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- """
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-
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- DATA_URL = "https://www.dropbox.com/s/djsk4kbu8in66gp/linguisticprobing.zip?dl=1"
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-
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- TASK_TO_LABELS = {
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- "subj_number": ["NN", "NNS"],
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- "word_content": [
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- "abandoned",
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- "abruptly",
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- "accent",
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- "access",
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- "according",
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- "account",
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- "ache",
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- "ached",
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- "acted",
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- "acting",
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- "actions",
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- "actual",
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- "address",
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- "advantage",
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- "advice",
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- "afford",
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- "agent",
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- "agreement",
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- "aiden",
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- "airport",
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- "alarm",
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- "albert",
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- "alert",
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- "alexander",
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- "alice",
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- "alley",
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- "allowing",
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- "aloud",
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- "amanda",
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- "amber",
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- "american",
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- "amused",
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- "amusement",
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- "ancient",
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- "andy",
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- "annie",
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- "announced",
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- "annoyed",
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- "answering",
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- "anticipation",
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- "anxious",
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- "anybody",
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- "apologize",
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- "appearance",
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- "appears",
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- "approach",
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- "approaching",
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- "arched",
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- "argue",
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- "argument",
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- "aria",
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- "arrive",
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- "ashe",
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- "assume",
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- "assured",
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- "attached",
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- "attacked",
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- "attempted",
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- "audience",
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- "available",
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- "awful",
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- "awkward",
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- "background",
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- "backpack",
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- "backward",
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- "backwards",
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- "bags",
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- "balance",
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- "bank",
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- "barn",
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- "bars",
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- "bastard",
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- "bath",
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- "beating",
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- "begged",
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- "begins",
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- "begun",
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- "behavior",
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- "bell",
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- "belly",
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- "belong",
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- "belonged",
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- "bench",
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- "biggest",
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- "bike",
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- "billy",
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- "bird",
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- "birds",
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- "birth",
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- "birthday",
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- "bitch",
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- "bitter",
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- "blank",
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- "blast",
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- "bleeding",
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- "blind",
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- "blocked",
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- "blond",
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- "blonde",
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- "bond",
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- "bone",
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- "bored",
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- "bothered",
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- "bound",
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- "bowed",
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- "bowl",
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- "boxes",
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- "branches",
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- "brave",
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- "bread",
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- "breast",
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- "breaths",
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- "brian",
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- "brick",
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- "brilliant",
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- "broad",
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- "brows",
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- "brush",
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- "brushing",
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- "buddy",
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- "build",
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- "buildings",
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- "bullet",
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- "bunch",
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- "butt",
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- "cage",
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- "cain",
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- "caleb",
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- "callum",
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- "calmly",
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- "cameron",
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- "capable",
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- "cards",
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- "career",
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- "carol",
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- "caroline",
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- "carriage",
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- "cash",
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- "cassidy",
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- "casual",
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- "catching",
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- "causing",
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- "centuries",
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- "chain",
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- "chairs",
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- "challenge",
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- "chamber",
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- "chances",
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- "changes",
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- "changing",
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- "charged",
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- "checking",
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- "chicken",
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- "chief",
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- "chill",
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- "chloe",
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- "chocolate",
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- "choked",
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- "chosen",
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- "christian",
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- "chuckle",
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- "cigarette",
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- "circles",
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- "circumstances",
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- "claim",
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- "claimed",
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- "clary",
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- "classes",
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- "claws",
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- "clay",
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- "cleaned",
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- "cleaning",
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- "clearing",
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- "clicked",
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- "cliff",
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- "climb",
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- "climbing",
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- "closely",
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- "closest",
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- "cloth",
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- "clothing",
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- "cloud",
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- "clue",
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- "clung",
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- "clutched",
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- "coach",
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- "cocked",
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- "code",
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- "colin",
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- "collapsed",
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- "collar",
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- "colors",
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- "commander",
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- "comment",
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- "complicated",
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- "concrete",
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- "condition",
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- "confidence",
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- "confident",
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- "confirmed",
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- "connected",
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- "connor",
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- "convince",
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- "convinced",
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- "cook",
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- "cops",
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- "copy",
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- "corners",
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- "correct",
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- "cost",
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- "courage",
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- "cousin",
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- "covering",
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- "covers",
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- "crack",
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- "cracked",
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- "crap",
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- "crash",
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- "crashed",
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- "crawled",
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- "cream",
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- "create",
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- "credit",
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- "crept",
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- "crime",
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- "crossing",
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- "crowded",
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- "cruel",
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- "crystal",
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- "cupped",
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- "curiosity",
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- "current",
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- "curse",
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- "cursed",
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- "cute",
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- "cutting",
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- "daemon",
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- "dagger",
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- "damned",
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- "damp",
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- "danced",
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- "danny",
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- "dante",
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- "darkened",
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- "darling",
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- "darted",
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- "dating",
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- "dave",
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- "dawn",
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- "dealing",
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- "delicate",
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- "delicious",
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- "della",
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- "demons",
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- "deny",
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- "department",
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- "desert",
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- "deserved",
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- "destroy",
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- "destroyed",
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- "detail",
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- "detective",
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- "devil",
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- "devon",
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- "digging",
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- "dining",
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- "direct",
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- "directions",
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- "disappear",
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- "disappointed",
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- "disappointment",
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- "disbelief",
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- "discuss",
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- "disgust",
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- "display",
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- "distant",
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- "distracted",
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- "dogs",
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- "dollars",
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- "double",
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- "doubted",
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- "dozen",
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- "drag",
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- "dragging",
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- "drake",
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- "drank",
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- "drawer",
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- "drinks",
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- "driveway",
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- "dropping",
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- "drops",
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- "drug",
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- "drugs",
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- "ducked",
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- "duke",
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- "dull",
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- "duncan",
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- "duty",
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- "dylan",
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- "eager",
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- "eased",
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- "east",
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- "eaten",
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- "echo",
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- "echoed",
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- "eddie",
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- "edges",
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- "elbow",
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- "elena",
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- "ellie",
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- "embarrassed",
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- "embrace",
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- "emerged",
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- "emergency",
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- "emotional",
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- "ends",
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- "enemies",
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- "enormous",
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- "envelope",
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- "equipment",
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- "evan",
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- "event",
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- "everybody",
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- "exact",
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- "exchange",
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- "exchanged",
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- "exclaimed",
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- "exhausted",
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- "exist",
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- "existed",
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- "existence",
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- "exit",
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- "expensive",
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- "experienced",
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- "explanation",
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- "exploded",
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- "exposed",
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- "extended",
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- "extremely",
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- "eyed",
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- "fabric",
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- "fade",
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- "fairly",
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- "faith",
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- "fake",
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- "falls",
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- "families",
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- "fangs",
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- "farm",
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- "farther",
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- "favor",
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- "feared",
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- "feed",
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- "fellow",
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- "fence",
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- "fierce",
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- "fifty",
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- "file",
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- "filling",
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- "fingertips",
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- "finn",
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- "fired",
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- "fixed",
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- "flame",
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- "flashlight",
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- "flicked",
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- "floating",
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- "flushed",
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- "foolish",
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- "football",
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- "footsteps",
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- "forcing",
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- "formed",
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- "frame",
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- "freedom",
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- "french",
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- "friday",
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- "friendly",
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- "frightened",
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- "frown",
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- "frowning",
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- "froze",
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- "frozen",
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- "frustrated",
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- "fuck",
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- "fucking",
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- "furious",
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- "furniture",
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- "fury",
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- "gabe",
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- "games",
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- "garage",
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- "garrett",
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- "gary",
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- "gasp",
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- "gates",
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- "gather",
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- "gathering",
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- "gavin",
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- "gear",
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- "gesture",
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- "gestured",
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- "ghost",
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- "girlfriend",
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- "glances",
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- "glare",
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- "glowing",
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- "goodbye",
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- "gorgeous",
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- "government",
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- "gown",
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- "grabbing",
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- "graham",
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- "grand",
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- "grandfather",
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- "grandma",
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- "grant",
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- "grasp",
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- "grave",
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- "greeted",
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- "grief",
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- "grinning",
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- "groan",
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- "growl",
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- "guessed",
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- "guest",
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- "guests",
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- "guns",
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- "gwen",
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- "halfway",
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- "hank",
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- "hanna",
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- "happily",
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- "happiness",
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- "harry",
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- "harsh",
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- "heal",
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- "heartbeat",
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- "heaven",
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- "height",
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- "hero",
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- "hesitation",
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- "hissed",
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- "hitting",
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- "holds",
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- "holiday",
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- "holly",
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- "holy",
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- "honor",
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- "hopefully",
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- "horizon",
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- "houses",
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- "humor",
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- "hundreds",
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- "hunger",
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- "hunt",
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- "hunting",
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- "hurting",
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- "ideas",
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- "idiot",
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- "ignoring",
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- "images",
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- "imagination",
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- "impressed",
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- "impression",
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- "inches",
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- "including",
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- "incredible",
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- "incredibly",
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- "informed",
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- "injured",
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- "inner",
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- "insane",
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- "intended",
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- "intensity",
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- "invisible",
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- "invited",
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- "iron",
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- "isaac",
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- "issue",
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- "items",
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- "jamie",
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- "jared",
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- "jealous",
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- "jeremy",
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- "jerk",
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- "jess",
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- "jesus",
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- "jimmy",
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- "joey",
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- "johnny",
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- "jonas",
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- "jordan",
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- "joseph",
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- "joshua",
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- "josie",
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- "journey",
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- "judge",
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- "jumping",
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- "kane",
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- "karen",
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- "katherine",
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- "katie",
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- "keeps",
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- "kevin",
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- "kicking",
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- "kidding",
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- "killer",
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- "kisses",
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- "knelt",
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- "knocking",
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- "ladies",
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- "landing",
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- "language",
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- "larger",
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- "lately",
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- "laura",
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- "lauren",
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- "lawyer",
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- "lean",
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- "leapt",
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- "learning",
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- "lesson",
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- "letters",
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- "licked",
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- "lies",
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- "lifting",
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- "lightning",
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- "likes",
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- "lined",
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- "liquid",
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- "lobby",
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- "location",
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- "london",
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- "lonely",
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- "louder",
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- "lover",
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- "loves",
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- "loving",
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- "lucien",
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- "lucy",
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- "major",
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- "mama",
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- "manner",
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- "marks",
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- "martin",
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- "mask",
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- "mason",
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- "mass",
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- "massive",
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- "mate",
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- "material",
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- "mattered",
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- "matters",
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- "meaning",
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- "meat",
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- "medical",
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- "melissa",
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- "member",
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- "members",
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- "mental",
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- "mere",
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- "merely",
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- "midnight",
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- "military",
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- "minds",
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- "miranda",
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- "mist",
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- "mixed",
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- "moaned",
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- "monday",
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- "moonlight",
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- "morgan",
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- "mortal",
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- "motioned",
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- "mountains",
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- "movements",
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- "moves",
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- "movies",
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- "mumbled",
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- "muscle",
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- "mystery",
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- "nails",
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- "named",
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- "nathan",
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- "nearest",
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- "necklace",
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- "needing",
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- "nerves",
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- "nervously",
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- "nicolas",
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- "nicole",
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- "nightmare",
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- "nights",
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- "nina",
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- "nodding",
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- "nods",
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- "nora",
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- "noted",
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- "notes",
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- "numbers",
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- "object",
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- "occurred",
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- "offering",
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- "officers",
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- "oliver",
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- "opens",
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- "opinion",
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- "option",
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- "orange",
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- "ordinary",
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- "original",
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- "ourselves",
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- "overhead",
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- "owner",
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- "packed",
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- "pages",
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- "painful",
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- "paint",
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- "painted",
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- "painting",
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- "palace",
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- "palms",
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- "paris",
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- "parted",
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- "particularly",
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- "partner",
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- "parts",
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- "passenger",
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- "passion",
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- "patch",
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- "patience",
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- "patient",
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- "patrick",
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- "patted",
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- "paying",
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- "period",
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- "permission",
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- "pete",
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- "photo",
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- "pile",
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- "pillow",
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- "pissed",
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- "pity",
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- "placing",
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- "plain",
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- "planet",
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- "plastic",
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- "pleasant",
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- "plus",
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- "pockets",
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- "points",
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- "polite",
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- "popped",
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- "positive",
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- "possibility",
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- "potential",
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- "pounded",
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- "powers",
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- "precious",
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- "prefer",
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- "prepare",
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- "president",
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- "preston",
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- "pretend",
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- "pretending",
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- "previous",
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- "price",
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- "pride",
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- "princess",
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- "prison",
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- "professor",
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- "program",
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- "progress",
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- "project",
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- "proof",
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- "proper",
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- "property",
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- "protection",
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- "protest",
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- "provide",
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- "provided",
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- "pulse",
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- "punch",
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- "punched",
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- "pure",
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- "purple",
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- "puts",
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- "quinn",
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- "quit",
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- "racing",
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- "radio",
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- "rafe",
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- "raise",
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- "raising",
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- "rapidly",
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- "rare",
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- "rarely",
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- "reaches",
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- "react",
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- "realizing",
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- "rear",
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- "rebecca",
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- "recall",
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- "received",
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- "recently",
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- "record",
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- "reflection",
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- "refuse",
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- "regular",
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- "reluctantly",
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- "remaining",
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- "remains",
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- "remembering",
772
- "remind",
773
- "remove",
774
- "replaced",
775
- "request",
776
- "required",
777
- "rescue",
778
- "research",
779
- "resist",
780
- "responsibility",
781
- "responsible",
782
- "result",
783
- "returning",
784
- "reveal",
785
- "revealed",
786
- "revealing",
787
- "ribs",
788
- "riding",
789
- "rifle",
790
- "roar",
791
- "robe",
792
- "robert",
793
- "rode",
794
- "rope",
795
- "rubbing",
796
- "ruin",
797
- "ruined",
798
- "rule",
799
- "runs",
800
- "rushing",
801
- "sadly",
802
- "sadness",
803
- "sake",
804
- "sally",
805
- "satisfaction",
806
- "satisfied",
807
- "saturday",
808
- "savannah",
809
- "saving",
810
- "scanned",
811
- "scar",
812
- "scare",
813
- "scattered",
814
- "scott",
815
- "scrambled",
816
- "screams",
817
- "seated",
818
- "seats",
819
- "secrets",
820
- "section",
821
- "sees",
822
- "self",
823
- "sell",
824
- "sensation",
825
- "sensed",
826
- "senses",
827
- "sentence",
828
- "separate",
829
- "serena",
830
- "series",
831
- "serve",
832
- "served",
833
- "settle",
834
- "sexy",
835
- "shade",
836
- "shakes",
837
- "shame",
838
- "sharply",
839
- "shattered",
840
- "sheet",
841
- "sheets",
842
- "sheriff",
843
- "shield",
844
- "shining",
845
- "shiver",
846
- "shivered",
847
- "shooting",
848
- "shopping",
849
- "shore",
850
- "shortly",
851
- "shorts",
852
- "shout",
853
- "shouting",
854
- "shown",
855
- "shrug",
856
- "shuddered",
857
- "sidewalk",
858
- "sideways",
859
- "signal",
860
- "signs",
861
- "silk",
862
- "silly",
863
- "similar",
864
- "sisters",
865
- "site",
866
- "sits",
867
- "skills",
868
- "skirt",
869
- "skull",
870
- "slapped",
871
- "sleeve",
872
- "slide",
873
- "slipping",
874
- "slowed",
875
- "slumped",
876
- "smaller",
877
- "smith",
878
- "snap",
879
- "social",
880
- "society",
881
- "sofa",
882
- "somewhat",
883
- "sooner",
884
- "sophia",
885
- "sophie",
886
- "souls",
887
- "sounding",
888
- "source",
889
- "spare",
890
- "speech",
891
- "spencer",
892
- "spending",
893
- "spinning",
894
- "split",
895
- "spring",
896
- "squeeze",
897
- "stands",
898
- "stated",
899
- "statement",
900
- "states",
901
- "steal",
902
- "stepping",
903
- "stiff",
904
- "stirred",
905
- "stole",
906
- "stones",
907
- "stops",
908
- "straightened",
909
- "stream",
910
- "stretch",
911
- "strike",
912
- "strode",
913
- "stroked",
914
- "struggle",
915
- "struggling",
916
- "student",
917
- "students",
918
- "studying",
919
- "stuffed",
920
- "style",
921
- "success",
922
- "suggest",
923
- "sunday",
924
- "sunlight",
925
- "supplies",
926
- "surprisingly",
927
- "surrounding",
928
- "survived",
929
- "suspect",
930
- "suspected",
931
- "suspicious",
932
- "swallow",
933
- "sweetheart",
934
- "swiftly",
935
- "swing",
936
- "switch",
937
- "t-shirt",
938
- "tables",
939
- "tail",
940
- "tapped",
941
- "tara",
942
- "target",
943
- "task",
944
- "tasted",
945
- "taught",
946
- "taylor",
947
- "teach",
948
- "teacher",
949
- "teased",
950
- "teasing",
951
- "television",
952
- "tells",
953
- "temper",
954
- "temple",
955
- "tense",
956
- "tent",
957
- "terms",
958
- "terrified",
959
- "terror",
960
- "tessa",
961
- "text",
962
- "thigh",
963
- "thighs",
964
- "thousands",
965
- "threatened",
966
- "threatening",
967
- "throughout",
968
- "thrown",
969
- "thrust",
970
- "tighter",
971
- "till",
972
- "tipped",
973
- "toby",
974
- "toes",
975
- "tony",
976
- "tore",
977
- "torn",
978
- "total",
979
- "tough",
980
- "towel",
981
- "tower",
982
- "trace",
983
- "tracks",
984
- "traffic",
985
- "trailed",
986
- "trained",
987
- "trap",
988
- "trapped",
989
- "traveled",
990
- "travis",
991
- "tray",
992
- "treat",
993
- "treated",
994
- "trembled",
995
- "trevor",
996
- "trick",
997
- "tristan",
998
- "trunk",
999
- "trusted",
1000
- "twelve",
1001
- "ugly",
1002
- "unconscious",
1003
- "unexpected",
1004
- "uniform",
1005
- "universe",
1006
- "unknown",
1007
- "unlike",
1008
- "unsure",
1009
- "unusual",
1010
- "upper",
1011
- "upright",
1012
- "upward",
1013
- "useful",
1014
- "useless",
1015
- "valley",
1016
- "vanished",
1017
- "various",
1018
- "vehicle",
1019
- "veins",
1020
- "victor",
1021
- "video",
1022
- "vincent",
1023
- "violet",
1024
- "visible",
1025
- "vulnerable",
1026
- "walks",
1027
- "wandered",
1028
- "warrior",
1029
- "wash",
1030
- "washed",
1031
- "waving",
1032
- "weather",
1033
- "werewolf",
1034
- "whipped",
1035
- "whispers",
1036
- "whom",
1037
- "willow",
1038
- "winced",
1039
- "wings",
1040
- "winked",
1041
- "wise",
1042
- "wishing",
1043
- "witch",
1044
- "wolves",
1045
- "worn",
1046
- "wounded",
1047
- "wounds",
1048
- "wrap",
1049
- "wrapping",
1050
- "wrists",
1051
- "wrote",
1052
- "xavier",
1053
- "yanked",
1054
- "yard",
1055
- "yards",
1056
- "yelling",
1057
- "zach",
1058
- ],
1059
- "obj_number": ["NN", "NNS"],
1060
- "past_present": ["PAST", "PRES"],
1061
- "sentence_length": [0, 1, 2, 3, 4, 5],
1062
- "top_constituents": [
1063
- "ADVP_NP_VP_.",
1064
- "CC_ADVP_NP_VP_.",
1065
- "CC_NP_VP_.",
1066
- "IN_NP_VP_.",
1067
- "NP_ADVP_VP_.",
1068
- "NP_NP_VP_.",
1069
- "NP_PP_.",
1070
- "NP_VP_.",
1071
- "OTHER",
1072
- "PP_NP_VP_.",
1073
- "RB_NP_VP_.",
1074
- "SBAR_NP_VP_.",
1075
- "SBAR_VP_.",
1076
- "S_CC_S_.",
1077
- "S_NP_VP_.",
1078
- "S_VP_.",
1079
- "VBD_NP_VP_.",
1080
- "VP_.",
1081
- "WHADVP_SQ_.",
1082
- "WHNP_SQ_.",
1083
- ],
1084
- "tree_depth": ["depth_5", "depth_6", "depth_7", "depth_8", "depth_9", "depth_10", "depth_11"],
1085
- "coordination_inversion": ["I", "O"],
1086
- "odd_man_out": ["C", "O"],
1087
- "bigram_shift": ["I", "O"],
1088
- }
1089
-
1090
-
1091
- def get_labels(task):
1092
- return TASK_TO_LABELS[task]
1093
-
1094
-
1095
- class LinguisticprobingConfig(datasets.BuilderConfig):
1096
- """BuilderConfig for Linguisticprobing."""
1097
-
1098
- def __init__(
1099
- self,
1100
- text_features,
1101
- label_classes=None,
1102
- process_label=lambda x: x,
1103
- **kwargs,
1104
- ):
1105
- """BuilderConfig for Linguisticprobing.
1106
- Args:
1107
- text_features: `dict[string, string]`, map from the name of the feature
1108
- dict for each text field to the name of the column in the tsv file
1109
- label_column: `string`, name of the column in the tsv file corresponding
1110
- to the label
1111
- data_url: `string`, url to download the zip file from
1112
- data_dir: `string`, the path to the folder containing the tsv files in the
1113
- downloaded zip
1114
- citation: `string`, citation for the data set
1115
- url: `string`, url for information about the data set
1116
- label_classes: `list[string]`, the list of classes if the label is
1117
- categorical. If not provided, then the label will be of type
1118
- `datasets.Value('float32')`.
1119
- process_label: `Function[string, any]`, function taking in the raw value
1120
- of the label and processing it to the form required by the label feature
1121
- **kwargs: keyword arguments forwarded to super.
1122
- """
1123
-
1124
- super(LinguisticprobingConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
1125
-
1126
- self.text_features = text_features
1127
- self.label_column = "label"
1128
- self.label_classes = get_labels(self.name)
1129
- self.data_url = DATA_URL
1130
- self.data_dir = os.path.join("linguisticprobing", self.name)
1131
- self.citation = textwrap.dedent(_Linguisticprobing_CITATION)
1132
- self.process_label = process_label
1133
- self.description = ""
1134
- self.url = ""
1135
-
1136
-
1137
- class Linguisticprobing(datasets.GeneratorBasedBuilder):
1138
-
1139
- """The General Language Understanding Evaluation (Linguisticprobing) benchmark."""
1140
-
1141
- BUILDER_CONFIG_CLASS = LinguisticprobingConfig
1142
-
1143
- BUILDER_CONFIGS = [
1144
- LinguisticprobingConfig(
1145
- name="subj_number",
1146
- text_features={"sentence": "sentence"},
1147
- ),
1148
- LinguisticprobingConfig(
1149
- name="word_content",
1150
- text_features={"sentence": "sentence"},
1151
- ),
1152
- LinguisticprobingConfig(
1153
- name="obj_number",
1154
- text_features={"sentence": "sentence"},
1155
- ),
1156
- LinguisticprobingConfig(
1157
- name="past_present",
1158
- text_features={"sentence": "sentence"},
1159
- ),
1160
- LinguisticprobingConfig(
1161
- name="sentence_length",
1162
- text_features={"sentence": "sentence"},
1163
- ),
1164
- LinguisticprobingConfig(
1165
- name="top_constituents",
1166
- text_features={"sentence": "sentence"},
1167
- ),
1168
- LinguisticprobingConfig(
1169
- name="tree_depth",
1170
- text_features={"sentence": "sentence"},
1171
- ),
1172
- LinguisticprobingConfig(
1173
- name="coordination_inversion",
1174
- text_features={"sentence": "sentence"},
1175
- ),
1176
- LinguisticprobingConfig(
1177
- name="odd_man_out",
1178
- text_features={"sentence": "sentence"},
1179
- ),
1180
- LinguisticprobingConfig(
1181
- name="bigram_shift",
1182
- text_features={"sentence": "sentence"},
1183
- ),
1184
- ]
1185
-
1186
- def _info(self):
1187
- features = {text_feature: datasets.Value("string") for text_feature in six.iterkeys(self.config.text_features)}
1188
- if self.config.label_classes:
1189
- features["label"] = datasets.features.ClassLabel(names=self.config.label_classes)
1190
- else:
1191
- features["label"] = datasets.Value("float32")
1192
- features["idx"] = datasets.Value("int32")
1193
- return datasets.DatasetInfo(
1194
- description=_Linguisticprobing_DESCRIPTION,
1195
- features=datasets.Features(features),
1196
- homepage=self.config.url,
1197
- citation=self.config.citation + "\n" + _Linguisticprobing_CITATION,
1198
- )
1199
-
1200
- def _split_generators(self, dl_manager):
1201
- dl_dir = dl_manager.download_and_extract(self.config.data_url)
1202
- data_dir = os.path.join(dl_dir, self.config.data_dir)
1203
-
1204
- return [
1205
- datasets.SplitGenerator(
1206
- name=datasets.Split.TRAIN,
1207
- gen_kwargs={
1208
- "data_file": os.path.join(data_dir or "", "train.tsv"),
1209
- "split": "train",
1210
- },
1211
- ),
1212
- datasets.SplitGenerator(
1213
- name=datasets.Split.VALIDATION,
1214
- gen_kwargs={
1215
- "data_file": os.path.join(data_dir or "", "dev.tsv"),
1216
- "split": "dev",
1217
- },
1218
- ),
1219
- datasets.SplitGenerator(
1220
- name=datasets.Split.TEST,
1221
- gen_kwargs={
1222
- "data_file": os.path.join(data_dir or "", "test.tsv"),
1223
- "split": "test",
1224
- },
1225
- ),
1226
- ]
1227
-
1228
- def _generate_examples(self, data_file, split):
1229
-
1230
- process_label = self.config.process_label
1231
- label_classes = self.config.label_classes
1232
-
1233
- with open(data_file, encoding="utf8") as f:
1234
- reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
1235
-
1236
- for n, row in enumerate(reader):
1237
-
1238
- example = {feat: row[col] for feat, col in six.iteritems(self.config.text_features)}
1239
- example["idx"] = n
1240
-
1241
- if self.config.label_column in row:
1242
- label = row[self.config.label_column]
1243
- if label_classes and label not in label_classes:
1244
- label = int(label) if label else None
1245
- example["label"] = process_label(label)
1246
- else:
1247
- example["label"] = process_label(-1)
1248
- yield example["idx"], example