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wit.py

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+# coding=utf-8
+# Copyright 2022 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
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{10.1145/3404835.3463257,
+    author = {Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
+    title = {WIT: Wikipedia-Based Image Text Dataset for Multimodal Multilingual Machine Learning},
+    year = {2021},
+    isbn = {9781450380379},
+    publisher = {Association for Computing Machinery},
+    address = {New York, NY, USA},
+    url = {https://doi.org/10.1145/3404835.3463257},
+    doi = {10.1145/3404835.3463257},
+    booktitle = {Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
+    pages = {2443–2449},
+    numpages = {7},
+    keywords = {dataset, multimodal, machine learning, wikipedia, multilingual, image-text retrieval, neural networks},
+    location = {Virtual Event, Canada},
+    series = {SIGIR '21}
+}
+"""
+
+_DATASETNAME = "wit"
+
+_DESCRIPTION = """\
+Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset.
+WIT is composed of a curated set of 37.6 million entity rich image-text examples with
+11.5 million unique images across 108 Wikipedia languages. There are more than 12k
+examples in each of 108 languages, with 53 languages having 100k image-text pairs.
+Nine languages are spoken in the Southeast Asian region.
+Since the dataset contains multiple references, following Section 3.2 of the dataset's
+paper, the `seacrowd_imtext` subsets specify which reference is used for each data
+instance's texts via context in metadata.
+"""
+
+_HOMEPAGE = "https://github.com/google-research-datasets/wit"
+
+_LANGUAGES = {"ceb": "ceb", "fil": "fil", "ind": "id", "jav": "jv", "zlm": "zlm", "mya": "my", "tha": "th", "vie": "vi", "war": "war"}
+
+_LANGUAGE_CODES = list(_LANGUAGES.values())
+
+_LICENSE = Licenses.CC_BY_SA_3_0.value
+
+_LOCAL = False
+
+_URLS = {
+    "train_0": "https://storage.googleapis.com/gresearch/wit/wit_v1.train.all-00000-of-00010.tsv.gz",
+    "train_1": "https://storage.googleapis.com/gresearch/wit/wit_v1.train.all-00001-of-00010.tsv.gz",
+    "train_2": "https://storage.googleapis.com/gresearch/wit/wit_v1.train.all-00002-of-00010.tsv.gz",
+    "train_3": "https://storage.googleapis.com/gresearch/wit/wit_v1.train.all-00003-of-00010.tsv.gz",
+    "train_4": "https://storage.googleapis.com/gresearch/wit/wit_v1.train.all-00004-of-00010.tsv.gz",
+    "train_5": "https://storage.googleapis.com/gresearch/wit/wit_v1.train.all-00005-of-00010.tsv.gz",
+    "train_6": "https://storage.googleapis.com/gresearch/wit/wit_v1.train.all-00006-of-00010.tsv.gz",
+    "train_7": "https://storage.googleapis.com/gresearch/wit/wit_v1.train.all-00007-of-00010.tsv.gz",
+    "train_8": "https://storage.googleapis.com/gresearch/wit/wit_v1.train.all-00008-of-00010.tsv.gz",
+    "train_9": "https://storage.googleapis.com/gresearch/wit/wit_v1.train.all-00009-of-00010.tsv.gz",
+    "test_0": "https://storage.googleapis.com/gresearch/wit/wit_v1.test.all-00000-of-00005.tsv.gz",
+    "test_1": "https://storage.googleapis.com/gresearch/wit/wit_v1.test.all-00001-of-00005.tsv.gz",
+    "test_2": "https://storage.googleapis.com/gresearch/wit/wit_v1.test.all-00002-of-00005.tsv.gz",
+    "test_3": "https://storage.googleapis.com/gresearch/wit/wit_v1.test.all-00003-of-00005.tsv.gz",
+    "test_4": "https://storage.googleapis.com/gresearch/wit/wit_v1.test.all-00004-of-00005.tsv.gz",
+    "val_0": "https://storage.googleapis.com/gresearch/wit/wit_v1.val.all-00000-of-00005.tsv.gz",
+    "val_1": "https://storage.googleapis.com/gresearch/wit/wit_v1.val.all-00001-of-00005.tsv.gz",
+    "val_2": "https://storage.googleapis.com/gresearch/wit/wit_v1.val.all-00002-of-00005.tsv.gz",
+    "val_3": "https://storage.googleapis.com/gresearch/wit/wit_v1.val.all-00003-of-00005.tsv.gz",
+    "val_4": "https://storage.googleapis.com/gresearch/wit/wit_v1.val.all-00004-of-00005.tsv.gz",
+}
+
+_SUPPORTED_TASKS = [Tasks.IMAGE_CAPTIONING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class WITDataset(datasets.GeneratorBasedBuilder):
+    """
+    WIT is an image-text dataset from https://huggingface.co/datasets/google/wit.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = (
+        [
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_source",
+                version=datasets.Version(_SOURCE_VERSION),
+                description=f"{_DATASETNAME} source schema for all 9 languages",
+                schema="source",
+                subset_id=f"{_DATASETNAME}",
+            )
+        ]
+        + [
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_seacrowd_imtext",
+                version=datasets.Version(_SEACROWD_VERSION),
+                description=f"{_DATASETNAME} SEACrowd schema for all 9 languages",
+                schema="seacrowd_imtext",
+                subset_id=f"{_DATASETNAME}",
+            )
+        ]
+        + [
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{lang}_source",
+                version=datasets.Version(_SOURCE_VERSION),
+                description=f"{_DATASETNAME}_{lang} source schema",
+                schema="source",
+                subset_id=f"{_DATASETNAME}_{lang}",
+            )
+            for lang in _LANGUAGES
+        ]
+        + [
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{lang}_seacrowd_imtext",
+                version=datasets.Version(_SEACROWD_VERSION),
+                description=f"{_DATASETNAME}_{lang} SEACrowd schema",
+                schema="seacrowd_imtext",
+                subset_id=f"{_DATASETNAME}_{lang}",
+            )
+            for lang in _LANGUAGES
+        ]
+    )
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "language": datasets.Value("string"),
+                    "page_url": datasets.Value("string"),
+                    "image_url": datasets.Value("string"),
+                    "page_title": datasets.Value("string"),
+                    "section_title": datasets.Value("string"),
+                    "hierarchical_section_title": datasets.Value("string"),
+                    "caption_reference_description": datasets.Value("string"),
+                    "caption_attribution_description": datasets.Value("string"),
+                    "caption_alt_text_description": datasets.Value("string"),
+                    "mime_type": datasets.Value("string"),
+                    "original_height": datasets.Value("int32"),
+                    "original_width": datasets.Value("int32"),
+                    "is_main_image": datasets.Value("bool"),
+                    "attribution_passes_lang_id": datasets.Value("bool"),
+                    "page_changed_recently": datasets.Value("bool"),
+                    "context_page_description": datasets.Value("string"),
+                    "context_section_description": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "seacrowd_imtext":
+            features = schemas.image_text_features()
+        else:
+            raise ValueError(f"Invalid schema: '{self.config.schema}'")
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """
+        Returns SplitGenerators.
+        """
+
+        train_paths = dl_manager.download_and_extract([v for k, v in _URLS.items() if "train" in k])
+        test_paths = dl_manager.download_and_extract([v for k, v in _URLS.items() if "test" in k])
+        val_paths = dl_manager.download_and_extract([v for k, v in _URLS.items() if "val" in k])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepaths": train_paths,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepaths": test_paths,
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepaths": val_paths,
+                    "split": "validation",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepaths: Path, split: str) -> Tuple[int, Dict]:
+        """
+        Yields examples as (key, example) tuples.
+        """
+        subset_id = self.config.subset_id.split("_")
+        if len(subset_id) > 1:
+            language_list = subset_id[1]
+            if language_list in _LANGUAGES:
+                language_list = [_LANGUAGES[language_list]]
+        else:
+            language_list = _LANGUAGE_CODES
+
+        idx = 0
+        for file in filepaths:
+            with open(
+                file,
+                "r",
+                encoding="utf-8",
+                newline="",
+            ) as f:
+                data = csv.DictReader(
+                    f,
+                    delimiter="\t",
+                    quoting=csv.QUOTE_ALL,
+                )
+                if self.config.schema == "seacrowd_imtext":
+                    for d in data:
+                        if d["language"] in language_list:
+                            text = None
+                            context = None
+                            if d["caption_reference_description"] != "":
+                                text = d["caption_reference_description"]
+                                context = "caption_reference_description"
+                            elif d["caption_attribution_description"] != "":
+                                text = d["caption_attribution_description"]
+                                context = "caption_attribution_description"
+                            else:
+                                text = d["caption_alt_text_description"]
+                                context = "caption_alt_text_description"
+                            x = {
+                                "id": idx,
+                                "image_paths": [d["image_url"]],
+                                "texts": text,
+                                "metadata": {
+                                    "context": context,
+                                    "labels": None,
+                                },
+                            }
+                            yield idx, x
+                            idx += 1
+
+                elif self.config.schema == "source":
+                    for d in data:
+                        if d["language"] in language_list:
+                            x = {k: v if v != "" and k in self.info.features else None for k, v in d.items()}
+                            yield idx, x
+                            idx += 1
+                else:
+                    raise ValueError(f"Invalid schema: '{self.config.schema}'")