initial data loader

README.md

@@ -1,3 +1,40 @@
 ---
 license: cc-by-sa-4.0
+dataset_info:
+  features:
+  - name: gem_id
+    dtype: string
+  - name: example_id
+    dtype: string
+  - name: title
+    dtype: string
+  - name: unit_of_measure
+    dtype: string
+  - name: chart_type
+    dtype: string
+  - name: was_translated
+    dtype: string
+  - name: table_data
+    dtype: string
+  - name: linearized_input
+    dtype: string
+  - name: table_text
+    sequence: string
+  - name: target
+    dtype: string
+  splits:
+  - name: ru
+    num_bytes: 308435
+    num_examples: 210
+  - name: test
+    num_bytes: 1691383
+    num_examples: 763
+  - name: train
+    num_bytes: 10019272
+    num_examples: 6962
+  - name: validation
+    num_bytes: 1598442
+    num_examples: 754
+  download_size: 18543506
+  dataset_size: 13617532
 ---

TaTA.py

@@ -0,0 +1,169 @@
+# coding=utf-8
+# Copyright 2020 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.
+"""Dataloader for TaTA: A Multilingual Table-to-Text Dataset for African Languages."""
+
+import json
+import os
+
+import datasets
+import re
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@misc{gehrmann2022TaTA,
+Author = {Sebastian Gehrmann and Sebastian Ruder and Vitaly Nikolaev and Jan A. Botha and Michael Chavinda and Ankur Parikh and Clara Rivera},
+Title = {TaTa: A Multilingual Table-to-Text Dataset for African Languages},
+Year = {2022},
+Eprint = {arXiv:2211.00142},
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+Dataset loader for TaTA: A Multilingual Table-to-Text Dataset for African Languages
+"""
+
+_HOMEPAGE = "https://github.com/google-research/url-nlp/tree/main/tata"
+
+_LICENSE = "CC-BY-SA 4.0"
+
+_URLs = {
+    "train": "https://raw.githubusercontent.com/google-research/url-nlp/main/tata/train.json",
+    "validation": "https://raw.githubusercontent.com/google-research/url-nlp/main/tata/dev.json",
+    "test": "https://raw.githubusercontent.com/google-research/url-nlp/main/tata/test.json",
+    "ru": "https://raw.githubusercontent.com/google-research/url-nlp/main/tata/ru.json"
+}
+
+
+class TaTA(datasets.GeneratorBasedBuilder):
+    """TaTA dataset builder."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    # BUILDER_CONFIGS = [
+    #     datasets.BuilderConfig(name="nlg_en", version=VERSION, description="NLG: Data-to-English text."),
+    #     datasets.BuilderConfig(name="nlg_de", version=VERSION, description="NLG: Data-to-German text."),
+    #     datasets.BuilderConfig(name="mt_en-de", version=VERSION, description="MT: English-to-German text."),
+    #     datasets.BuilderConfig(name="mt_de-en", version=VERSION, description="MT: German-to-English text."),
+    #     datasets.BuilderConfig(name="nlg+mt_en-de", version=VERSION, description="NLG+MT: Data+English-to-German text."),
+    #     datasets.BuilderConfig(name="nlg+mt_de-en", version=VERSION, description="NLG+MT: Data+German-to-English text."),
+    # ]
+
+    def _info(self):
+        # max 26 entries in each box_score field.
+        features = datasets.Features(
+            {
+                "gem_id": datasets.Value("string"),
+                "example_id": datasets.Value("string"),
+                "title": datasets.Value("string"),
+                "unit_of_measure": datasets.Value("string"),
+                "chart_type": datasets.Value("string"),
+                "was_translated": datasets.Value("string"),
+                "table_data": datasets.Value("string"), # datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
+                "linearized_input": datasets.Value("string"),
+                # This field has all the references in a list.
+                "table_text": datasets.Sequence(datasets.Value("string")),
+                # Only use `target` as supervised key, not for evaluation!
+                "target": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=("linearized_input", "target"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        data_dir = dl_manager.download_and_extract(_URLs)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir["train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir["test"],
+                    "split": "test"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir["validation"],
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name="ru",
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir["ru"],
+                    "split": "ru",
+                },
+            ),
+        ]
+
+
+    def _generate_examples(
+        self, filepath, split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    ):
+        """ Yields examples as (key, example) tuples. """
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is here for legacy reason (tfds) and is not important in itself.
+
+        with open(filepath, encoding="utf-8") as f:
+            all_data = json.load(f)
+            for id_, data in enumerate(all_data):
+                data['gem_id'] = data['example_id']
+                if not data['table_text']:
+                  data['target'] = ""
+                else:
+                  data['target'] = data['table_text'][0]
+                yield id_, data