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odinsynth_sequence_dataset.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.
+"""Dataset builder script for the Odinsynth sequence generation dataset"""
+
+
+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{huggingface:dataset,
+# title = {A great new dataset},
+# author={huggingface, Inc.
+# },
+# year={2020}
+# }
+# """
+
+
+# You can copy an official description
+_DESCRIPTION = """
+Dataset for Odinsynth sequence data generation
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+_LICENSE = ""
+
+
+class OdinsynthSequenceDataset(datasets.GeneratorBasedBuilder):
+    """This contains a dataset for odinsynth rule synthesis in a supervised manner"""
+
+    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="synthetic_surface",
+                               version=VERSION,
+                               description="Synthetic data with surface rules only"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "synthetic_surface"
+
+    def _info(self):
+
+        features = datasets.Features(
+            {
+                "rule": datasets.Value("string"),
+                "spec": datasets.Sequence(
+                    {
+                        "sentence": datasets.Value("string"),
+                        "matched_text": datasets.Value("string"),
+                        "words": datasets.Sequence(datasets.Value("string")),
+                        "match_start": datasets.Value("int16"),
+                        "match_end": datasets.Value("int16"),
+                    }
+                ),
+                "generation_info": [
+                    {
+                        "transitions": datasets.Sequence(datasets.Value("string")),
+                        "generation_rules": datasets.Sequence(datasets.Value("string")),
+                        "delexicalized_generation_rules": datasets.Sequence(datasets.Value("string")),
+                        "innermost_substitutions": datasets.Sequence(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):
+        # 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
+
+        data_path = dl_manager.download_and_extract("out.jsonl.gz")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_path,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # 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 key, row in enumerate(f):
+                data = json.loads(row)
+                yield key, {
+                    "rule": data["rule"],
+                    "spec": [
+                        {
+                            "sentence":     s[0],
+                            "matched_text": s[1],
+                            "words":        s[3],
+                            "match_start":  s[4],
+                            "match_end":    s[5]
+                        }
+                        for s in data['spec']
+                    ],
+                    "generation_info": [
+                        {
+                            "transitions":                      i["transitions"],
+                            "generation_rules":                 i["generation_rules"],
+                            "delexicalized_generation_rules":   i["delexicalized_generation_rules"],
+                            "innermost_substitutions":          i["innermost_substitutions"],
+                        }
+                        for i in data['generation_info']
+                    ]
+                }

out.jsonl.gz

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+oid sha256:8ad9505cd0526bea591491b88f7964a6f2f53e4f68d06333c3a1db4a4da97b5d
+size 108196388