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HausaVQA.py

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-# 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.
-
-"""HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language."""
-
-
-import os
-import datasets
-from tqdm import tqdm
-
-_CITATION = """\
-@inproceedings{parida-etal-2023-havqa,
-    title = "{H}a{VQA}: A Dataset for Visual Question Answering and Multimodal Research in {H}ausa Language",
-    author = "Parida, Shantipriya  and
-      Abdulmumin, Idris  and
-      Muhammad, Shamsuddeen Hassan  and
-      Bose, Aneesh  and
-      Kohli, Guneet Singh  and
-      Ahmad, Ibrahim Said  and
-      Kotwal, Ketan  and
-      Deb Sarkar, Sayan  and
-      Bojar, Ond{\v{r}}ej  and
-      Kakudi, Habeebah",
-    editor = "Rogers, Anna  and
-      Boyd-Graber, Jordan  and
-      Okazaki, Naoaki",
-    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
-    month = jul,
-    year = "2023",
-    address = "Toronto, Canada",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2023.findings-acl.646",
-    doi = "10.18653/v1/2023.findings-acl.646",
-    pages = "10162--10183"
-}
-"""
-
-
-_DESCRIPTION = """\
-This paper presents “HaVQA”, the first multimodal dataset for visual question answering (VQA) tasks in the Hausa language. The dataset was created by manually translating 6,022 English question-answer pairs, which are associated with 1,555 unique images from the Visual Genome dataset. As a result, the dataset provides 12,044 gold standard English-Hausa parallel sentences that were translated in a fashion that guarantees their semantic match with the corresponding visual information. We conducted several baseline experiments on the dataset, including visual question answering, visual question elicitation, text-only and multimodal machine translation.
-"""
-
-_HOMEPAGE = "https://github.com/abumafrim/HausaVQA"
-_LICENSE = "https://creativecommons.org/licenses/by-nc-sa/4.0/"
-_URL = "https://raw.githubusercontent.com/abumafrim/HausaVQA/main/"
-
-class HausaVQAClass(datasets.GeneratorBasedBuilder):
-    """HausaVQA dataset"""
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "image": datasets.Image(),
-                "qa_id": datasets.Value("int32"),
-                "ques_en": datasets.Value("string"),
-                "ans_en": datasets.Value("string"),
-                "ques_ha": datasets.Value("string"),
-                "ans_ha": datasets.Value("string")
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        train_data = dl_manager.download(os.path.join(_URL, "hausa-visual-question-answer-train.txt"))
-        valid_data = dl_manager.download(os.path.join(_URL, "hausa-visual-question-answer-dev.txt"))
-        test_data = dl_manager.download(os.path.join(_URL, "hausa-visual-question-answer-test.txt"))
-
-        image_ids = set()
-        for filepath in [train_data, valid_data, test_data]:
-            with open(filepath, encoding="utf-8") as f:
-                data = f.read().splitlines()
-                for item in data:
-                    image_id, _, _, _, _, _ = item.split("\t")
-                    image_ids.add(image_id)
-
-        images_path = {}
-        for id in tqdm(image_ids, desc="Downloading images"):
-            images_path[id] = dl_manager.download(os.path.join(_URL, "hausa-visual-question-answer-images", id + '.jpg'))
-            
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": train_data,
-                    "image_paths": images_path
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": valid_data,
-                    "image_paths": images_path
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": test_data,
-                    "image_paths": images_path
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, image_paths):
-        with open(filepath, encoding="utf-8") as f:
-            data = f.read().splitlines()
-            for row, item in enumerate(data):
-                image_id, qa_id, ques_en, ans_en, ques_ha, ans_ha = item.split("\t")
-                yield row, {"image": image_paths[image_id], "qa_id": qa_id, "ques_en": ques_en, "ans_en": ans_en, "ques_ha": ques_ha, "ans_ha": ans_ha}

data/hausa_vqa-test.arrow

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data/hausa_vqa-train.arrow

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data/hausa_vqa-validation.arrow

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