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add loading script

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  1. Caltech-101.py +207 -0
Caltech-101.py ADDED
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+ # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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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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+ # TODO: Address all TODOs and remove all explanatory comments
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+ """TODO: Add a description here."""
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+
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+
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+ import csv
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+ import json
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+ import os
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+
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+ import datasets
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+ from datasets.tasks import ImageClassification
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+
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+ _CITATION = """\
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+ @article{FeiFei2004LearningGV,
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+ title={Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories},
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+ author={Li Fei-Fei and Rob Fergus and Pietro Perona},
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+ journal={Computer Vision and Pattern Recognition Workshop},
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+ year={2004},
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+ }
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+ """
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+
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+ _DESCRIPTION = """\
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+ Pictures of objects belonging to 101 categories.
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+ About 40 to 800 images per category.
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+ Most categories have about 50 images.
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+ Collected in September 2003 by Fei-Fei Li, Marco Andreetto, and Marc'Aurelio Ranzato.
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+ The size of each image is roughly 300 x 200 pixels.
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+ """
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+
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+ _HOMEPAGE = "https://data.caltech.edu/records/20086"
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+
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+ _LICENSE = "CC BY 4.0"
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+
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+ _DATA_URL = "brand_new_data/caltech-101.zip"
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+ # _DATA_URL = "brand_new_data/caltech-101/101_ObjectCategories.tar.gz"
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+
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+ _NAMES = [
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+ "accordion",
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+ "airplanes",
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+ "anchor",
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+ "ant",
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+ "background_google",
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+ "barrel",
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+ "bass",
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+ "beaver",
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+ "binocular",
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+ "bonsai",
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+ "brain",
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+ "brontosaurus",
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+ "buddha",
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+ "butterfly",
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+ "camera",
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+ "cannon",
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+ "car_side",
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+ "ceiling_fan",
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+ "cellphone",
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+ "chair",
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+ "chandelier",
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+ "cougar_body",
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+ "cougar_face",
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+ "crab",
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+ "crayfish",
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+ "crocodile",
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+ "crocodile_head",
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+ "cup",
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+ "dalmatian",
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+ "dollar_bill",
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+ "dolphin",
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+ "dragonfly",
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+ "electric_guitar",
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+ "elephant",
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+ "emu",
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+ "euphonium",
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+ "ewer",
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+ "faces",
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+ "faces_easy",
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+ "ferry",
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+ "flamingo",
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+ "flamingo_head",
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+ "garfield",
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+ "gerenuk",
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+ "gramophone",
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+ "grand_piano",
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+ "hawksbill",
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+ "headphone",
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+ "hedgehog",
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+ "helicopter",
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+ "ibis",
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+ "inline_skate",
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+ "joshua_tree",
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+ "kangaroo",
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+ "ketch",
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+ "lamp",
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+ "laptop",
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+ "leopards",
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+ "llama",
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+ "lobster",
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+ "lotus",
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+ "mandolin",
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+ "mayfly",
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+ "menorah",
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+ "metronome",
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+ "minaret",
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+ "motorbikes",
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+ "nautilus",
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+ "octopus",
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+ "okapi",
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+ "pagoda",
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+ "panda",
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+ "pigeon",
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+ "pizza",
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+ "platypus",
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+ "pyramid",
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+ "revolver",
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+ "rhino",
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+ "rooster",
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+ "saxophone",
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+ "schooner",
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+ "scissors",
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+ "scorpion",
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+ "sea_horse",
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+ "snoopy",
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+ "soccer_ball",
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+ "stapler",
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+ "starfish",
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+ "stegosaurus",
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+ "stop_sign",
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+ "strawberry",
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+ "sunflower",
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+ "tick",
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+ "trilobite",
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+ "umbrella",
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+ "watch",
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+ "water_lilly",
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+ "wheelchair",
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+ "wild_cat",
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+ "windsor_chair",
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+ "wrench",
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+ "yin_yang",
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+ ]
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+
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+ _TRAIN_POINTS_PER_CLASS = 30
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+
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+
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+ class Caltech101(datasets.GeneratorBasedBuilder):
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+ """Caltech 101 dataset."""
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+
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+ VERSION = datasets.Version("1.0.0")
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+
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+ def _info(self):
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=datasets.Features(
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+ {
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+ "img": datasets.Image(),
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+ "label": datasets.features.ClassLabel(names=_NAMES),
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+ }
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+ ),
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+ supervised_keys=("img", "label"),
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+ homepage=_HOMEPAGE,
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+ license=_LICENSE,
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+ citation=_CITATION,
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+ task_templates=ImageClassification(
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+ image_column="img", label_column="label"
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+ ),
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ # ----- Work in progress here -----
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+ data_dir = dl_manager.download_and_extract(_DATA_URL)
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+ files = dl_manager.iter_files(data_dir)
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+ # ---------------------------------
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={
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+ "filepath": data_dir, # TODO: change accordingly
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+ "split": "train",
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+ },
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TEST,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={
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+ "filepath": data_dir, # TODO: change accordingly
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+ "split": "test",
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+ },
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+ ),
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+ ]
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+
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+ # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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+ def _generate_examples(self, filepath, split):
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+ # TODO
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+ pass