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conceptual_12m_indo.py

@@ -1,82 +0,0 @@
-# coding=utf-8
-# 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.
-"""Conceptual 12M dataset."""
-
-import datasets
-import gzip
-import json
-
-
-_CITATION = """\
-@inproceedings{changpinyo2021cc12m,
-  title = {{Conceptual 12M}: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts},
-  author = {Changpinyo, Soravit and Sharma, Piyush and Ding, Nan and Soricut, Radu},
-  booktitle = {CVPR},
-  year = {2021},
-}
-"""
-
-_DESCRIPTION = """\
-Conceptual 12M is a large-scale dataset of 12 million
-image-text pairs specifically meant to be used for visionand-language pre-training.
-Its data collection pipeline is a relaxed version of the one used in Conceptual Captions 3M.
-"""
-
-_HOMEPAGE = "https://github.com/google-research-datasets/conceptual-12m"
-
-_LICENSE = """\
-The dataset may be freely used for any purpose, although acknowledgement of
-Google LLC ("Google") as the data source would be appreciated. The dataset is
-provided "AS IS" without any warranty, express or implied. Google disclaims all
-liability for any damages, direct or indirect, resulting from the use of the
-dataset.
-"""
-
-_URL = "https://huggingface.co/datasets/munggok/CC_12M_Indonesia/resolve/main/data/cc12m_indo.jsonl.gz"
-
-
-class Conceptual12M(datasets.GeneratorBasedBuilder):
-    """Conceptual 12M Indonesia dataset."""
-
-    def _info(self):
-        features = datasets.Features({"image_url": datasets.Value("string"), "caption": datasets.Value("string")})
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        file = dl_manager.download(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "file": file,
-                },
-            ),
-        ]
-
-    def _generate_examples(self, file):
-        id_ = 0
-        with gzip.open(open(file, "rb"), "rt", encoding="utf-8") as fi:
-            for line in fi:
-                if line:
-                    example = json.loads(line)
-                    yield id_, {"image_url": example['url'], "caption": example['caption']}
-                    id_ += 1

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