Uploading data builder

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}

README.md

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 ---
 license: cc-by-sa-4.0
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: qa_id
+    dtype: int32
+  - name: ques_en
+    dtype: string
+  - name: ans_en
+    dtype: string
+  - name: ques_ha
+    dtype: string
+  - name: ans_ha
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1068111
+    num_examples: 4816
+  - name: validation
+    num_bytes: 132120
+    num_examples: 602
+  - name: test
+    num_bytes: 131229
+    num_examples: 602
+  download_size: 205165688
+  dataset_size: 1331460
 ---