data

deepa2-corpus.py

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+# 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
+"""Dataset with argument reconstructions (DA2) manually gathered from the literature."""
+
+import logging
+import json
+import os
+
+logger = logging.getLogger(__name__)
+
+import datasets  # pylint: disable=import-error
+from huggingface_hub import hf_hub_download  # pylint: disable=import-error
+
+
+
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{betz:dataset,
+title = {A great new dataset},
+author={Betz, Gregor
+},
+year={2020}
+}
+"""
+
+_DESCRIPTION = """\
+Dataset with argument reconstructions (DA2) manually gathered from the literature.
+"""
+
+_HOMEPAGE = ""
+
+_LICENSE = ""
+
+_URLS = {
+    "all": ["corpus.zip"],
+    "books_only": ["corpus.zip"],
+}
+
+
+class DeepA2Corpus(datasets.GeneratorBasedBuilder):
+    """Dataset with argument reconstructions (DA2) manually gathered from the literature."""
+
+    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="all", version=VERSION, description="This part of my dataset covers a first domain"),
+        datasets.BuilderConfig(name="books_only", version=VERSION, description="NOT IMPLEMENTED YET"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "all"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "text": 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, 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
+
+        # 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
+        
+        #filenames = _URLS[self.config.name]
+        #
+        #filepaths = []
+        #for filename in filenames:
+        #    logger.info("Downloading %s", filename)
+        #    filepaths.append(
+        #        hf_hub_download(
+        #            repo_id="debatelab/deepa2-harvest",
+        #            repo_type="dataset",
+        #            filename=filename,
+        #            token=True,
+        #        )
+        #    )
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepaths": ["corpus"],
+                    "split": datasets.Split.TRAIN,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepaths": [],
+                    "split": datasets.Split.VALIDATION,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepaths": [],
+                    "split": datasets.Split.TEST,
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepaths, 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.
+
+
+        for filepath in filepaths:
+            dataset = datasets.load_dataset("debatelab/deepa2-corpus", datafiles=f"{filepath}/*.txt",split=split)
+
+
+            with open(filepath, encoding="utf-8") as f:
+                for key, row in enumerate(f):
+                    data = json.loads(row)
+                    data = {
+                        k:v for k,v in data.items()
+                        if k in _DA2_FEATURES
+                    }
+                    data["metadata"] = []
+                    yield key, data