init: conversational weather data

README.md

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+TODO

conversational_weather.py

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+# 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.
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{balakrishnan-etal-2019-constrained,
+  title = "Constrained Decoding for Neural {NLG} from Compositional Representations in Task-Oriented Dialogue",
+  author = "Balakrishnan, Anusha  and
+    Rao, Jinfeng  and
+    Upasani, Kartikeya  and
+    White, Michael  and
+    Subba, Rajen",
+  booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
+  month = jul,
+  year = "2019",
+  address = "Florence, Italy",
+  publisher = "Association for Computational Linguistics",
+  url = "https://www.aclweb.org/anthology/P19-1080",
+  doi = "10.18653/v1/P19-1080",
+  pages = "831--844"
+}
+"""
+
+_DESCRIPTION = """\
+The Conversational Weather dataset is designed for generation of responses to weather queries based on a structured input data. The input allows specifying data attributes such as dates, times, locations, weather conditions, and errors, and also offers control over structure of response through discourse relations such as join, contrast, and justification.
+"""
+
+_HOMEPAGE = "https://github.com/facebookresearch/TreeNLG"
+
+_LICENSE = "CC-BY-NC-4.0"
+
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLs = {
+    'weather': {
+        'train': 'https://raw.githubusercontent.com/facebookresearch/TreeNLG/master/data/weather/train.tsv',
+        'validation': 'https://raw.githubusercontent.com/facebookresearch/TreeNLG/master/data/weather/val.tsv',
+        'test': 'https://raw.githubusercontent.com/facebookresearch/TreeNLG/master/data/weather/test.tsv'
+    }
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class ConversationalWeather(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    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="weather", version=VERSION, description="This part of my dataset covers a first domain"),
+        #datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "weather"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        if self.config.name == "weather":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "gem_id": datasets.Value("string"),
+                    "data_id": datasets.Value("string"),
+                    "user_query": datasets.Value("string"),
+                    "tree_str_mr": datasets.Value("string"),
+                    "response": datasets.Value("string"),
+                    # These are the features of your dataset like images, labels ...
+                }
+            )
+        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
+            features = datasets.Features({})
+
+        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=None,
+            # 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."""
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # 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
+        my_urls = _URLs[self.config.name]
+        print(my_urls)
+        data_dir = dl_manager.download_and_extract(my_urls)
+        print(data_dir)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir['train']),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir['test']),
+                    "split": "test"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir['validation']),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    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:
+            csv_reader = csv.reader(f, delimiter='\t')
+            for id_, row in enumerate(csv_reader):
+                if self.config.name == "weather":
+                    assert len(row) == 4
+                    yield id_, {
+                        "gem_id": f"{self.config.name}-{split}-{id_}",
+                        "data_id": row[0],
+                        "user_query": row[1],
+                        "tree_str_mr": row[2],
+                        "response": row[3],
+                    }

dataset_infos.json

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+{'todo': 'true'}