Convert dataset to Parquet

README.md

@@ -47,10 +47,10 @@ dataset_info:
       dtype: string
   splits:
   - name: train
-    num_bytes: 2976697816
+    num_bytes: 2976665740
     num_examples: 8904060
-  download_size: 710727536
-  dataset_size: 2976697816
+  download_size: 898478552
+  dataset_size: 2976665740
 - config_name: open
   features:
   - name: subject
@@ -77,10 +77,20 @@ dataset_info:
       dtype: string
   splits:
   - name: train
-    num_bytes: 2882678298
+    num_bytes: 2882646222
     num_examples: 8904060
-  download_size: 710727536
-  dataset_size: 2882678298
+  download_size: 900156754
+  dataset_size: 2882646222
+configs:
+- config_name: canonical
+  data_files:
+  - split: train
+    path: canonical/train-*
+  default: true
+- config_name: open
+  data_files:
+  - split: train
+    path: open/train-*
 ---
 
 # Dataset Card for Ascent KB

ascent_kb.py

@@ -1,147 +0,0 @@
-# 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.
-"""Ascent KB: A Deep Commonsense Knowledge Base"""
-
-import json
-
-import datasets
-
-
-_CITATION = """\
-@InProceedings{nguyen2021www,
-  title={Advanced Semantics for Commonsense Knowledge Extraction},
-  author={Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard},
-  year={2021},
-  booktitle={The Web Conference 2021},
-}
-"""
-
-_DESCRIPTION = """\
-This dataset contains 8.9M commonsense assertions extracted by the Ascent pipeline (https://ascent.mpi-inf.mpg.de/).
-"""
-
-_HOMEPAGE = "https://ascent.mpi-inf.mpg.de/"
-
-_LICENSE = "The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/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)
-
-_URL = "https://nextcloud.mpi-klsb.mpg.de/index.php/s/dFLdTQHqiFrt3Q3/download"
-
-
-# DONE: Name of the dataset usually match the script name with CamelCase instead of snake_case
-class AscentKB(datasets.GeneratorBasedBuilder):
-    """Ascent KB: A Deep Commonsense Knowledge Base. Version 1.0.0."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="canonical",
-            version=VERSION,
-            description="This KB contains <arg1 ; rel ; arg2> \
-                               assertions where relations are canonicalized based on ConceptNet relations.",
-        ),
-        datasets.BuilderConfig(
-            name="open",
-            version=VERSION,
-            description="This KB contains open assertions of the form \
-                               <subject ; predicate ; object> extracted directly from web contents.",
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "canonical"
-
-    def _info(self):
-        if self.config.name == "canonical":
-            features = datasets.Features(
-                {
-                    "arg1": datasets.Value("string"),
-                    "rel": datasets.Value("string"),
-                    "arg2": datasets.Value("string"),
-                    "support": datasets.Value("int64"),
-                    "facets": [
-                        {
-                            "value": datasets.Value("string"),
-                            "type": datasets.Value("string"),
-                            "support": datasets.Value("int64"),
-                        }
-                    ],
-                    "source_sentences": [{"text": datasets.Value("string"), "source": datasets.Value("string")}],
-                }
-            )
-        else:  # features for the "open" part
-            features = datasets.Features(
-                {
-                    "subject": datasets.Value("string"),
-                    "predicate": datasets.Value("string"),
-                    "object": datasets.Value("string"),
-                    "support": datasets.Value("int64"),
-                    "facets": [
-                        {
-                            "value": datasets.Value("string"),
-                            "type": datasets.Value("string"),
-                            "support": datasets.Value("int64"),
-                        }
-                    ],
-                    "source_sentences": [{"text": datasets.Value("string"), "source": datasets.Value("string")}],
-                }
-            )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # my_urls = _URLs[self.config.name]
-        # data_file = dl_manager.download_and_extract(my_urls)
-
-        data_file = dl_manager.download_and_extract(_URL)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": data_file,
-                    "split": "train",
-                },
-            ),
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        """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 id_, row in enumerate(f):
-                data = json.loads(row)
-                if self.config.name == "canonical":
-                    data.pop("subject")
-                    data.pop("predicate")
-                    data.pop("object")
-                    yield id_, data
-                else:  # "open"
-                    data.pop("arg1")
-                    data.pop("rel")
-                    data.pop("arg2")
-                    yield id_, data

canonical/train-00000-of-00006.parquet

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dataset_infos.json

@@ -1 +0,0 @@
-{"canonical": {"description": "This dataset contains 8.9M commonsense assertions extracted by the Ascent pipeline (https://ascent.mpi-inf.mpg.de/).\n", "citation": "@InProceedings{nguyen2021www,\n  title={Advanced Semantics for Commonsense Knowledge Extraction},\n  author={Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard},\n  year={2021},\n  booktitle={The Web Conference 2021},\n}\n", "homepage": "https://ascent.mpi-inf.mpg.de/", "license": "The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/", "features": {"arg1": {"dtype": "string", "id": null, "_type": "Value"}, "rel": {"dtype": "string", "id": null, "_type": "Value"}, "arg2": {"dtype": "string", "id": null, "_type": "Value"}, "support": {"dtype": "int64", "id": null, "_type": "Value"}, "facets": [{"value": {"dtype": "string", "id": null, "_type": "Value"}, "type": {"dtype": "string", "id": null, "_type": "Value"}, "support": {"dtype": "int64", "id": null, "_type": "Value"}}], "source_sentences": [{"text": {"dtype": "string", "id": null, "_type": "Value"}, "source": {"dtype": "string", "id": null, "_type": "Value"}}]}, "post_processed": null, "supervised_keys": null, "builder_name": "ascent_kb", "config_name": "canonical", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2976697816, "num_examples": 8904060, "dataset_name": "ascent_kb"}}, "download_checksums": {"https://nextcloud.mpi-klsb.mpg.de/index.php/s/dFLdTQHqiFrt3Q3/download": {"num_bytes": 710727536, "checksum": "51fd88a07bca4fa48a9157dd1d93d9bac88ad2b38b5eae662d2cbfad47895016"}}, "download_size": 710727536, "post_processing_size": null, "dataset_size": 2976697816, "size_in_bytes": 3687425352}, "open": {"description": "This dataset contains 8.9M commonsense assertions extracted by the Ascent pipeline (https://ascent.mpi-inf.mpg.de/).\n", "citation": "@InProceedings{nguyen2021www,\n  title={Advanced Semantics for Commonsense Knowledge Extraction},\n  author={Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard},\n  year={2021},\n  booktitle={The Web Conference 2021},\n}\n", "homepage": "https://ascent.mpi-inf.mpg.de/", "license": "The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/", "features": {"subject": {"dtype": "string", "id": null, "_type": "Value"}, "predicate": {"dtype": "string", "id": null, "_type": "Value"}, "object": {"dtype": "string", "id": null, "_type": "Value"}, "support": {"dtype": "int64", "id": null, "_type": "Value"}, "facets": [{"value": {"dtype": "string", "id": null, "_type": "Value"}, "type": {"dtype": "string", "id": null, "_type": "Value"}, "support": {"dtype": "int64", "id": null, "_type": "Value"}}], "source_sentences": [{"text": {"dtype": "string", "id": null, "_type": "Value"}, "source": {"dtype": "string", "id": null, "_type": "Value"}}]}, "post_processed": null, "supervised_keys": null, "builder_name": "ascent_kb", "config_name": "open", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2882678298, "num_examples": 8904060, "dataset_name": "ascent_kb"}}, "download_checksums": {"https://nextcloud.mpi-klsb.mpg.de/index.php/s/dFLdTQHqiFrt3Q3/download": {"num_bytes": 710727536, "checksum": "51fd88a07bca4fa48a9157dd1d93d9bac88ad2b38b5eae662d2cbfad47895016"}}, "download_size": 710727536, "post_processing_size": null, "dataset_size": 2882678298, "size_in_bytes": 3593405834}}

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