Added datasetinfo

canard_quretec.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.
+
+
+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{Elgohary:Peskov:Boyd-Graber-2019,
+  Title = {Can You Unpack That? Learning to Rewrite Questions-in-Context},
+  Author = {Ahmed Elgohary and Denis Peskov and Jordan Boyd-Graber},
+  Booktitle = {Empirical Methods in Natural Language Processing},
+  Year = {2019}
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+CANARD has been preprocessed by Voskarides et al. to train and evaluate their Query Resolution Term Classification
+model (QuReTeC).
+
+CANARD is a dataset for question-in-context rewriting that consists of questions each given in a dialog context
+together with a context-independent rewriting of the question. The context of each question is the dialog utterences
+that precede the question. CANARD can be used to evaluate question rewriting models that handle important linguistic
+phenomena such as coreference and ellipsis resolution.
+"""
+
+_HOMEPAGE = "https://sites.google.com/view/qanta/projects/canard"
+
+_LICENSE = "CC BY-SA 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 = {
+    'voskarides': "https://drive.google.com/drive/folders/1e3s-V6VQqOKHrmn_kBStNsV0gGHPeJVf",
+}
+
+
+class CanardQuretec(datasets.GeneratorBasedBuilder):
+    """
+    Voskarides et al. have preprocessed CANARD in different ways depending on their experiment.
+    """
+
+    VERSION = datasets.Version("1.0.0")
+
+    # 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="gold_supervision", version=VERSION, description="Was used for training quretec with gold supervision"),
+        # datasets.BuilderConfig(name="original_all", version=VERSION, description="Was used for creating dataset statistics"),
+    ]
+
+    # It's not mandatory to have a default configuration. Just use one if it make sense.
+    DEFAULT_CONFIG_NAME = "gold_supervision"
+
+    def _info(self):
+        # This is the name of the configuration selected in BUILDER_CONFIGS above
+        # if self.config.name == "gold_supervision" or self.config.name == "original_all":
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "prev_questions": datasets.Value("string"),
+                "cur_question": datasets.Value("string"),
+                "gold_terms": datasets.features.Sequence(feature=datasets.Value('string')),
+                "semantic_terms": datasets.features.Sequence(feature=datasets.Value('string')),
+                "overlapping_terms": datasets.features.Sequence(feature=datasets.Value('string')),
+                "answer_text_with_window": datasets.Value("string"),
+                "answer_text": datasets.Value("string"),
+                "bert_ner_overlap": datasets.Array2D(shape=(2,), dtype="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=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,
+                gen_kwargs={  # These kwargs will be passed to _generate_examples
+                    "filepath": os.path.join(data_dir, "train_gold_supervision.json"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={  # These kwargs will be passed to _generate_examples
+                    "filepath": os.path.join(data_dir, "test_gold_supervision.json"),
+                    "split": "test"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={  # These kwargs will be passed to _generate_examples
+                    "filepath": os.path.join(data_dir, "dev_gold_supervision.json"),
+                    "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) as f:
+            data = json.load(f)
+            for id_, row in data:
+                # if self.config.name == "first_domain":
+                yield id_, row