data loadedr and zip files

.gitattributes

@@ -25,3 +25,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+company.zip filter=lfs diff=lfs merge=lfs -text
+film.zip filter=lfs diff=lfs merge=lfs -text
+animal.zip filter=lfs diff=lfs merge=lfs -text

animal.zip

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+oid sha256:ea645b57037b28a37189c1c3c137da96c42e7ec0f155eabd1a0852b0bf734e11
+size 490578232

company.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:114a90827867a579b6f7594a1c9a380a03062bdeb2055edc45acce2118c711e1
+size 868517018

film.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0473cf3ed18874809f7cd556c10e8c9ffba2b5f359a5d6c77ef1c53da2e92a9f
+size 855640260

wiki_cat_sum.py

@@ -0,0 +1,166 @@
+# 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
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@inproceedings{perez2019generating,
+  title={Generating Summaries with Topic Templates and Structured Convolutional Decoders},
+  author={Perez-Beltrachini, Laura and Liu, Yang and Lapata, Mirella},
+  booktitle={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
+  pages={5107--5116},
+  year={2019}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://datashare.ed.ac.uk/handle/10283/3368"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# 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 = {
+    # 'animals': "https://datashare.ed.ac.uk/bitstream/handle/10283/3368/animal_tok_min5_L7.5k.zip",
+    "animals": "https://huggingface.co/datasets/GEM/wiki_cat_sum/animal.zip"
+    'company': "https://huggingface.co/datasets/GEM/wiki_cat_sum/company.zip",
+    'film'   : "https://huggingface.co/datasets/GEM/wiki_cat_sum/film.zip",
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class WikiCatSum(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("0.1.0")
+
+    # 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="animal" , version=VERSION, description="Animal domain"),
+        datasets.BuilderConfig(name="company", version=VERSION, description="Company domain"),
+        datasets.BuilderConfig(name="film"   , version=VERSION, description="Film domain"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "animal"  # 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
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "title": datasets.Value("string"),
+                "paragraphs": datasets.features.Sequence(
+                    datasets.Value("string")),
+                "summary": datasets.features.Sequence(
+                    {
+                        "text": datasets.Value("string"),
+                        "topic": datasets.Value("int"),
+                    })
+                # These are the features of your dataset like images, labels ...
+            }
+        )
+        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]
+        data_dir = dl_manager.download_and_extract(my_urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train-%s.jsonl" % (self.config.name)),
+                    "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-%s.jsonl" % (self.config.name)),
+                    "split": "test"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "valid-%s.jsonl" % (self.config.name)),
+                    "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:
+            for row in f:
+                data = json.loads(row)
+                data["gem_id"] = "GEM-wiki_cat_sum-%s-%d" % (split,data["id"]+1)
+                yield data["id"],data