ScienceQA

ScienceQA.py

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+# Copyright 2020 The HuggingFace Datasets Authors.
+#
+# 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.
+
+"""ScienceQA loading script."""
+
+
+import json
+from pathlib import Path
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{lu2022learn,
+    title={Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering},
+    author={Lu, Pan and Mishra, Swaroop and Xia, Tony and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Ashwin Kalyan},
+    booktitle={The 36th Conference on Neural Information Processing Systems (NeurIPS)},
+    year={2022}
+}
+"""
+
+_DESCRIPTION = """\
+This is the ScienceQA dataset.
+"""
+
+_HOMEPAGE = "https://scienceqa.github.io/"
+
+_LICENSE = "CC BY-NC-SA (Attribution-NonCommercial-ShareAlike)"
+
+_URLS = {
+    "pid_splits": "https://drive.google.com/uc?id=1OXlNBuW74dsrwYZIpQMshFqxkjcMPPgV&export=download",
+    "problems": "https://drive.google.com/uc?id=1nJ86OLnF2C6eDoi5UOAdTAS5Duc0wuTl&export=download",
+    "train": "https://drive.google.com/uc?id=1swX4Eei1ZqrXRvM-JAZxN6QVwcBLPHV8&export=download",
+    "val": "https://drive.google.com/uc?id=1ijThWZc1tsoqGrOCWhYYj1HUJ48Hl8Zz&export=download",
+    "test": "https://drive.google.com/uc?id=1eyjFaHxbvEJZzdZILn3vnTihBNDmKcIj&export=download",
+}
+
+_SUB_FOLDER_OR_FILE_NAME = {
+    "pid_splits": "pid_splits.json",
+    "problems": "problems.json",
+    "train": "train",
+    "val": "val",
+    "test": "test",
+}
+
+
+class ScienceQADataset(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "question": datasets.Value("string"),
+                "choices": datasets.Sequence(datasets.Value("string")),
+                "answer": datasets.Value("int32"),
+                "hint": datasets.Value("string"),
+                "image": datasets.Image(),
+                "task": datasets.Value("string"),
+                "grade": datasets.Value("string"),
+                "subject": datasets.Value("string"),
+                "topic": datasets.Value("string"),
+                "category": datasets.Value("string"),
+                "skill": datasets.Value("string"),
+                "lecture": datasets.Value("string"),
+                "solution": datasets.Value("string"),
+                "split": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(_URLS)
+        gen_kwargs = {}
+        for split_name in ["train", "val", "test"]:
+            gen_kwargs_per_split = {}
+            gen_kwargs_per_split["pid_splits_path"] = Path(data_dir["pid_splits"]) / _SUB_FOLDER_OR_FILE_NAME["pid_splits"]
+            gen_kwargs_per_split["problems_path"] = Path(data_dir["problems"]) / _SUB_FOLDER_OR_FILE_NAME["problems"]
+            gen_kwargs_per_split["images_path"] = Path(data_dir[split_name]) / _SUB_FOLDER_OR_FILE_NAME[split_name]
+            gen_kwargs_per_split["split_name"] = split_name
+            gen_kwargs[split_name] = gen_kwargs_per_split
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs=gen_kwargs["train"],
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs=gen_kwargs["val"],
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs=gen_kwargs["test"],
+            ),
+        ]
+
+    def _generate_examples(self, pid_splits_path, problems_path, images_path, split_name):
+        pid_splits = json.load(open(pid_splits_path, "r"))
+        problems = json.load(open(problems_path, "r"))
+
+        for idx, key in enumerate(pid_splits[split_name]):
+            example = problems[key]
+            if example["image"]:
+                example["image"] = os.path.join(images_path, key, example["image"])
+            yield idx, example