Convert dataset to Parquet (#2)

- Convert dataset to Parquet (8f3028acd62f35553fa92d9cf541eb68d23b7ccd)
- Add extractive data files (6958faa6cf2e2fb2b33dbad376304accd30bd86a)
- Delete loading script (5ee2de3c67b3c9cd8e1c52282ea8215e9abdbbb2)
- Delete legacy dataset_infos.json (116f04f85f8fe2f5a892665c0134e56e4ef56860)

README.md

@@ -39,16 +39,16 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 6434909
+    num_bytes: 6434893
     num_examples: 6253
   - name: test
-    num_bytes: 843181
+    num_bytes: 843165
     num_examples: 811
   - name: validation
-    num_bytes: 689109
+    num_bytes: 689093
     num_examples: 661
-  download_size: 7755161
-  dataset_size: 7967199
+  download_size: 5167854
+  dataset_size: 7967151
 - config_name: extractive
   features:
   - name: query
@@ -59,16 +59,33 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 6434909
+    num_bytes: 6434893
     num_examples: 6253
   - name: test
-    num_bytes: 843181
+    num_bytes: 843165
     num_examples: 811
   - name: validation
-    num_bytes: 689109
+    num_bytes: 689093
     num_examples: 661
-  download_size: 7755161
-  dataset_size: 7967199
+  download_size: 5162151
+  dataset_size: 7967151
+configs:
+- config_name: abstractive
+  data_files:
+  - split: train
+    path: abstractive/train-*
+  - split: test
+    path: abstractive/test-*
+  - split: validation
+    path: abstractive/validation-*
+- config_name: extractive
+  data_files:
+  - split: train
+    path: extractive/train-*
+  - split: test
+    path: extractive/test-*
+  - split: validation
+    path: extractive/validation-*
 ---
 
 # Dataset Card for AQuaMuSe

abstractive/test-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0233791433515f5e906da847e3a808e157f8a1bb3891ae68126476f8c95ddf24
+size 543352

abstractive/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:13484e28ade720920ee06fb1e1c4e5be4e9bb7ce95e73625aef5883a667dc4ed
+size 4177513

abstractive/validation-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7c1fa26da92681958b9ebe263b95918b144902bdb7dce3672b1cf9520317ecef
+size 446989

aquamuse.py

@@ -1,154 +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.
-"""AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)"""
-
-
-import os
-from os import listdir
-from os.path import isfile, join
-
-import tensorflow as tf
-
-import datasets
-
-
-_CITATION = """\
-@misc{kulkarni2020aquamuse,
-      title={AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization},
-      author={Sayali Kulkarni and Sheide Chammas and Wan Zhu and Fei Sha and Eugene Ie},
-      year={2020},
-      eprint={2010.12694},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-"""
-
-_DESCRIPTION = """AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)"""
-
-_HOMEPAGE = "https://github.com/google-research-datasets/aquamuse"
-
-_LICENSE = ""
-
-zipped_data_url = "https://github.com/google-research-datasets/aquamuse/raw/main/v2/aquamuse_v2.zip"
-
-
-class Aquamuse(datasets.GeneratorBasedBuilder):
-    """Dataset for Query-based Multi-Document Summarization"""
-
-    VERSION = datasets.Version("2.3.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="abstractive", version=VERSION, description="Abstractive query-based multi-document summarization"
-        ),
-        datasets.BuilderConfig(
-            name="extractive", version=VERSION, description="Extractive query-based multi-document summarization"
-        ),
-    ]
-
-    # DEFAULT_CONFIG_NAME = "abstractive"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "query": datasets.Value("string"),
-                "input_urls": datasets.Sequence(datasets.Value("string")),
-                "target": 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."""
-
-        if self.config.name == "abstractive":
-            data_dir = dl_manager.download_and_extract(zipped_data_url)
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "v2.3/abstractive/train/"),
-                        "split": "train",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "v2.3/abstractive/test/"),
-                        "split": "test",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "v2.3/abstractive/dev/"),
-                        "split": "dev",
-                    },
-                ),
-            ]
-
-        else:
-            data_dir = dl_manager.download_and_extract(zipped_data_url)
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "v2.3/extractive/train/"),
-                        "split": "train",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "v2.3/extractive/test/"),
-                        "split": "test",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "v2.3/extractive/dev/"),
-                        "split": "dev",
-                    },
-                ),
-            ]
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples."""
-        filepath = [join(filepath, f) for f in listdir(filepath) if isfile(join(filepath, f))]
-        filepath = sorted(filepath)
-        raw_dataset = tf.data.TFRecordDataset(filepath)
-        for id_, raw_record in enumerate(raw_dataset):
-            example = tf.train.Example()
-            example.ParseFromString(raw_record.numpy())
-            yield id_, {
-                "query": example.features.feature["query"].bytes_list.value[0].decode(),
-                "input_urls": example.features.feature["input_urls"].bytes_list.value[0].decode().split("<EOD>"),
-                "target": example.features.feature["target"].bytes_list.value[0].decode(),
-            }

dataset_infos.json

@@ -1 +0,0 @@
-{"abstractive": {"description": "AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)", "citation": "@misc{kulkarni2020aquamuse,title={AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization}, author={Sayali Kulkarni and Sheide Chammas and Wan Zhu and Fei Sha and Eugene Ie}, year={2020}, eprint={2010.12694}, archivePrefix={arXiv}, primaryClass={cs.CL}}", "homepage": "https://github.com/google-research-datasets/aquamuse", "license": "", "features": {"query": {"dtype": "string", "id": null, "_type": "Value"}, "input_urls": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "target": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "aquamuse", "config_name": "abstractive", "version": {"version_str": "2.3.0", "description": null, "major": 2, "minor": 3, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 6434909, "num_examples": 6253, "dataset_name": "aquamuse"}, "test": {"name": "test", "num_bytes": 843181, "num_examples": 811, "dataset_name": "aquamuse"}, "validation": {"name": "validation", "num_bytes": 689109, "num_examples": 661, "dataset_name": "aquamuse"}}, "download_checksums": {"https://github.com/google-research-datasets/aquamuse/raw/main/v2/aquamuse_v2.zip": {"num_bytes": 7755161, "checksum": "f2b4d9523031a986e545a7c0fdc8180670519696340d09179a39514fc76466d0"}}, "download_size": 7755161, "post_processing_size": null, "dataset_size": 7967199, "size_in_bytes": 15722360}, "extractive": {"description": "AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)", "citation": "@misc{kulkarni2020aquamuse,title={AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization}, author={Sayali Kulkarni and Sheide Chammas and Wan Zhu and Fei Sha and Eugene Ie}, year={2020}, eprint={2010.12694}, archivePrefix={arXiv}, primaryClass={cs.CL}}", "homepage": "https://github.com/google-research-datasets/aquamuse", "license": "", "features": {"query": {"dtype": "string", "id": null, "_type": "Value"}, "input_urls": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "target": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "aquamuse", "config_name": "extractive", "version": {"version_str": "2.3.0", "description": null, "major": 2, "minor": 3, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 6434909, "num_examples": 6253, "dataset_name": "aquamuse"}, "test": {"name": "test", "num_bytes": 843181, "num_examples": 811, "dataset_name": "aquamuse"}, "validation": {"name": "validation", "num_bytes": 689109, "num_examples": 661, "dataset_name": "aquamuse"}}, "download_checksums": {"https://github.com/google-research-datasets/aquamuse/raw/main/v2/aquamuse_v2.zip": {"num_bytes": 7755161, "checksum": "f2b4d9523031a986e545a7c0fdc8180670519696340d09179a39514fc76466d0"}}, "download_size": 7755161, "post_processing_size": null, "dataset_size": 7967199, "size_in_bytes": 15722360}}

extractive/test-00000-of-00001.parquet

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+size 543635

extractive/train-00000-of-00001.parquet

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+oid sha256:24fb5984380b3319a1824bcb623eda558835c3142c2107930c97956c029ee1ab
+size 4172824

extractive/validation-00000-of-00001.parquet

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+size 445692