New balanced dataset

VQG.py

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+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and 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.
+
+# Lint as: python3
+"""SQUAD: The Stanford Question Answering Dataset."""
+
+
+import json
+
+import datasets
+from datasets.tasks import QuestionAnsweringExtractive
+import pandas as pd
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@article{2016arXiv160605250R,
+       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
+                 Konstantin and {Liang}, Percy},
+        title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
+      journal = {arXiv e-prints},
+         year = 2016,
+          eid = {arXiv:1606.05250},
+        pages = {arXiv:1606.05250},
+archivePrefix = {arXiv},
+       eprint = {1606.05250},
+}
+"""
+
+_DESCRIPTION = """\
+Visual questions for data science
+"""
+
+_URL = "https://huggingface.co/datasets/eduvedras/VQG/resolve/main/images.tar.gz"
+
+_METADATA_URLS = {
+                "train": "https://huggingface.co/datasets/eduvedras/VQG/resolve/main/metadata_train.csv", 
+                "validation": "https://huggingface.co/datasets/eduvedras/VQG/resolve/main/metadata_validation.csv",
+                "test": "https://huggingface.co/datasets/eduvedras/VQG/resolve/main/metadata_test.csv"
+            },
+
+class VQGTargz(datasets.GeneratorBasedBuilder):
+    """SQUAD: The Stanford Question Answering Dataset. Version 1.1."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "Id": datasets.Value("string"),
+                    "Question": datasets.Value("string"),
+                    "Chart": datasets.Image(),
+                    "Chart_name": datasets.Value("string"),
+                }
+            ),
+            # No default supervised_keys (as we have to pass both question
+            # and context as input).
+            supervised_keys=None,
+            homepage="https://huggingface.co/datasets/eduvedras/VQG",
+            citation=_CITATION,
+            task_templates=[
+                QuestionAnsweringExtractive(
+                    question_column="question", context_column="context", answers_column="answers"
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        path = dl_manager.download(_URL)
+        image_iters = dl_manager.iter_archive(path)
+        #split_metadata_path = dl_manager.download(_METADATA_URLS)
+        metadata_train_path = "https://huggingface.co/datasets/eduvedras/VQG/resolve/main/metadata_train.csv" 
+        metadata_validation_path = "https://huggingface.co/datasets/eduvedras/VQG/resolve/main/metadata_validation.csv"
+        metadata_test_path = "https://huggingface.co/datasets/eduvedras/VQG/resolve/main/metadata_test.csv"
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"images": image_iters,
+                                                                           "metadata_path": metadata_train_path}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"images": image_iters,
+                                                                           "metadata_path": metadata_validation_path}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"images": image_iters,
+                                                                           "metadata_path": metadata_test_path}),
+        ]
+
+    def _generate_examples(self, images, metadata_path):
+        """This function returns the examples in the raw (text) form."""
+        metadata = pd.read_csv(metadata_path)
+        idx = 0
+        for index, row in metadata.iterrows():
+            for filepath, image in images:
+                filepath = filepath.split('/')[-1]
+                if row['Chart'] in filepath:
+                    yield idx, {
+                        "Chart": {"path": filepath, "bytes": image.read()},
+                        "Question": row['Question'],
+                        "Id": row['Id'],
+                        "Chart_name": row['Chart'],
+                    }
+                    break
+            idx += 1

images.tar.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0b8a9b93accf013b9174e2cbfc817ce2c0d4dc1ded6e929061fd53908fd4b5b9
+size 133