Initial data loader version

.gitattributes

@@ -35,3 +35,10 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.mp3 filter=lfs diff=lfs merge=lfs -text
 *.ogg filter=lfs diff=lfs merge=lfs -text
 *.wav filter=lfs diff=lfs merge=lfs -text
+data/v1/test.jsonl filter=lfs diff=lfs merge=lfs -text
+data/v1/train.jsonl filter=lfs diff=lfs merge=lfs -text
+data/v1/validation.jsonl filter=lfs diff=lfs merge=lfs -text
+data/v1-1/*.jsonl filter=lfs diff=lfs merge=lfs -text
+data/v1-1/train.jsonl filter=lfs diff=lfs merge=lfs -text
+data/v1-1/validation.jsonl filter=lfs diff=lfs merge=lfs -text
+data/v1-1/test.jsonl filter=lfs diff=lfs merge=lfs -text

squality.py

@@ -0,0 +1,194 @@
+# 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: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+""" Dataset loading script for SQuALITY, an abstractive summarization dataset that is
+* long document: 3k-6k words
+* question-focused: 5/doc
+* multi-reference 4/question
+ """
+
+import os
+import csv
+import json
+
+import datasets
+
+
+_CITATION = """\
+@article{wang2022squality,
+  title={SQuALITY: Building a Long-Document Summarization Dataset the Hard Way},
+  author={Wang, Alex and Pang, Richard Yuanzhe and Chen, Angelica and Phang, Jason and Bowman, Samuel R.},
+  journal={arXiv preprint 2205.11465},
+  year={2022}
+}
+"""
+
+# 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.
+"""
+
+_HOMEPAGE = "ihttps://github.com/nyu-mll/SQuALITY"
+
+_LICENSE = "CC BY"
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+#_URLS = {
+#    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
+#    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
+#}
+
+
+class SQuALITYDataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1")
+
+    # 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')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="squality-v1", version=datasets.Version("1.0"), description="SQUALITY v1.0, containing 100 stories (2000 summaries)"),
+        datasets.BuilderConfig(name="squality-v1.1", version=VERSION, description="SQuALITY version v1.1, expands on v1.0 by adding 27 stories (540 summaries)"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "squality-v1.1"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+
+        #if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+        #    features = datasets.Features(
+        #        {
+        #            "sentence": datasets.Value("string"),
+        #            "option1": datasets.Value("string"),
+        #            "answer": datasets.Value("string")
+        #            # These are the features of your dataset like images, labels ...
+        #        }
+        #    )
+
+        features = datasets.Features(
+	    {
+		"document": datasets.Value("string"),
+		"question": datasets.Value("string"),
+		"summary": datasets.Value("string")
+		# 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,
+            # If there's a common (input, target) tuple from the features,
+            # uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # 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):
+        # 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
+
+        if self.config.name == "squality-v1":
+            data_dir = "data/v1"
+        elif self.config.name == "squality-v1.1":
+            data_dir = "data/v1-1"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train.jsonl"),
+                    "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.jsonl"),
+                    "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.jsonl"),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        with open(filepath, encoding="utf-8") as f:
+            for row in enumerate(f):
+                # fields
+                # * metadata
+                # * document
+                # * questions
+                story = json.loads(row)
+                for question in story['questions']:
+                    # fields
+                    # * question_text
+                    # * question_number
+                    # * responses
+                    key = question['gem_id']
+
+                    # for the test split, yield all references at once
+                    # to easily compute multi-reference metrics
+                    if split == "test":
+                        yield key, {
+                            'document': story['document'],
+                            'question': question['question_text'],
+                            'summary': [r['response_text'] for r in question['responses']]
+                        }
+
+                    else:
+                        for response in question['responses']:
+                            # fields
+                            # * uid
+                            # * worker_uid
+                            # * response_text
+                            yield key, {
+                                'document': story['document'],
+                                'question': question['question_text'],
+                                'summary': response['response_text']
+                            }
+