RenderedSST2.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""


import csv
import json
import os
from pathlib import Path

import datasets

_CITATION = """\
@article{DBLP:journals/corr/abs-2103-00020,
  author    = {Alec Radford and
               Jong Wook Kim and
               Chris Hallacy and
               Aditya Ramesh and
               Gabriel Goh and
               Sandhini Agarwal and
               Girish Sastry and
               Amanda Askell and
               Pamela Mishkin and
               Jack Clark and
               Gretchen Krueger and
               Ilya Sutskever},
  title     = {Learning Transferable Visual Models From Natural Language Supervision},
  journal   = {CoRR},
  volume    = {abs/2103.00020},
  year      = {2021},
  url       = {https://arxiv.org/abs/2103.00020},
  eprinttype = {arXiv},
  eprint    = {2103.00020},
  timestamp = {Thu, 04 Mar 2021 17:00:40 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2103-00020.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

_HOMEPAGE = "https://github.com/openai/CLIP/blob/main/data/rendered-sst2.md"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

_URL = "https://openaipublic.azureedge.net/clip/data/rendered-sst2.tgz"

_NAMES = ["negative", "positive"]


class SST2Dataset(datasets.GeneratorBasedBuilder):

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        features = datasets.Features(
            {
                "image": datasets.Image(),
                "label": datasets.ClassLabel(names=_NAMES),
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_dir = dl_manager.download_and_extract(_URL)
        data_dir = Path(data_dir) / "rendered-sst2"
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "dir": data_dir / "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "dir": data_dir / "valid",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "dir": data_dir / "test",
                },
            ),
        ]

    def _generate_examples(self, dir):
        index = -1
        for image_path in (dir / "negative").iterdir():
            index += 1
            record = {"label": "negative", "image": str(image_path)}
            yield index, record
        for image_path in (dir / "positive").iterdir():
            index += 1
            record = {"label": "positive", "image": str(image_path)}
            yield index, record