Delete WikiDO.py

WikiDO.py

@@ -1,173 +0,0 @@
-# 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.
-
-
-import csv
-import json
-import os
-
-import datasets
-
-
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@InProceedings{huggingface:dataset,
-title = {A great new dataset},
-author={huggingface, Inc.
-},
-year={2020}
-}
-"""
-
-# You can copy an official description
-_DESCRIPTION = """\
-To address this gap, we introduce WikiDO (drawn from Wikipedia Ddiversity Observatory), a new cross-modal retrieval benchmark to assess the OOD generalization capabilities of pretrained VLMs. 
-This consists of 380K image-text pairs from Wikipedia with domain labels, along with carefully curated, human-verified in-distribution (ID) and OOD test sets of size 3K each. 
-The image-text pairs are very diverse in topics.
-"""
-
-# Add a link to an official homepage for the dataset here
-_HOMEPAGE = "https://neurips-wikido.github.io/WIKIDO/"
-
-# Add the licence for the dataset here if you can find it
-_LICENSE = "CC-by-SA-4.0"
-
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URLS = {
-    "images":"./images.zip",
-}
-
-
-# Name of the dataset usually matches the script name with CamelCase instead of snake_case
-class WikiDO(datasets.GeneratorBasedBuilder):
-    """ Short description of my dataset."""
-
-    VERSION = datasets.Version("1.1.0")
-
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-
-    # DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
-    def _info(self):
-        # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        features = datasets.Features(
-            {
-                "image": datasets.Value("string"),
-                "orig_cap": datasets.Value("string"),
-                "caption": datasets.Value("string"),
-                "topic": datasets.Value("string"),
-                "page_id": datasets.Value("string"),
-                "page_title": datasets.Value("string")
-                }
-            )
-
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        # Define the local paths to the dataset files
-        data_dir = os.path.dirname(__file__)
-        
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "train_100k.jsonl"),
-                    "split": "train_100k",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "train_200k.jsonl"),
-                    "split": "train_200k",
-                },
-            ),
-            # datasets.SplitGenerator(
-            #     name=datasets.Split.TRAIN,
-            #     gen_kwargs={
-            #         "filepath": os.path.join(data_dir, "train_full.jsonl"),
-            #         "split": "train_full",
-            #     },
-            # ),
-            # datasets.SplitGenerator(
-            #     name=datasets.Split.VALIDATION,
-            #     gen_kwargs={
-            #         "filepath": os.path.join(data_dir, "val_full.jsonl"),
-            #         "split": "val_full",
-            #     },
-            # ),
-            # datasets.SplitGenerator(
-            #     name=datasets.Split.VALIDATION,
-            #     gen_kwargs={
-            #         "filepath": os.path.join(data_dir, "val_verified.jsonl"),
-            #         "split": "val_verified(1k)",
-            #     },
-            # ),
-            # datasets.SplitGenerator(
-            #     name=datasets.Split.TEST,
-            #     gen_kwargs={
-            #         "filepath": os.path.join(data_dir, "id_test_verified.jsonl"),
-            #         "split": "id_test_verified(3k)",
-            #     },
-            # ),
-            # datasets.SplitGenerator(
-            #     name=datasets.Split.TEST,
-            #     gen_kwargs={
-            #         "filepath": os.path.join(data_dir, "ood_test_full.jsonl"),
-            #         "split": "ood_test_full",
-            #     },
-            # ),
-            # datasets.SplitGenerator(
-            #     name=datasets.Split.TEST,
-            #     gen_kwargs={
-            #         "filepath": os.path.join(data_dir, "ood_test_verified.jsonl"),
-            #         "split": "ood_test_verified(3k)",
-            #     },
-            # ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        # Open the file and read the data
-        with open(filepath, encoding="utf-8") as f:
-            for id_, line in enumerate(f):
-                data = json.loads(line)
-                yield id_, data