Convert dataset to Parquet (#3)

- Convert dataset to Parquet (f302f6055b7549e97b4e658976f57dae7c3bae91)
- Delete loading script (7a396a7058bb654c8a7fa8059eece5f1f2206624)
- Delete legacy dataset_infos.json (1b5f364c1b85eceb9f142b373109a2f3d5f9fe99)

README.md

@@ -19,6 +19,7 @@ task_ids:
 - sentiment-classification
 pretty_name: Amazon Review Polarity
 dataset_info:
+  config_name: amazon_polarity
   features:
   - name: label
     dtype:
@@ -30,7 +31,6 @@ dataset_info:
     dtype: string
   - name: content
     dtype: string
-  config_name: amazon_polarity
   splits:
   - name: train
     num_bytes: 1604364432
@@ -38,8 +38,16 @@ dataset_info:
   - name: test
     num_bytes: 178176193
     num_examples: 400000
-  download_size: 688339454
+  download_size: 1145430497
   dataset_size: 1782540625
+configs:
+- config_name: amazon_polarity
+  data_files:
+  - split: train
+    path: amazon_polarity/train-*
+  - split: test
+    path: amazon_polarity/test-*
+  default: true
 train-eval-index:
 - config: amazon_polarity
   task: text-classification

amazon_polarity.py

@@ -1,126 +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.
-"""The amazon polarity dataset for text classification."""
-
-
-import csv
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{mcauley2013hidden,
-  title={Hidden factors and hidden topics: understanding rating dimensions with review text},
-  author={McAuley, Julian and Leskovec, Jure},
-  booktitle={Proceedings of the 7th ACM conference on Recommender systems},
-  pages={165--172},
-  year={2013}
-}
-"""
-
-_DESCRIPTION = """\
-The Amazon reviews dataset consists of reviews from amazon.
-The data span a period of 18 years, including ~35 million reviews up to March 2013.
-Reviews include product and user information, ratings, and a plaintext review.
-"""
-
-_HOMEPAGE = "https://registry.opendata.aws/"
-
-_LICENSE = "Apache License 2.0"
-
-_URLs = {
-    "amazon_polarity": "https://s3.amazonaws.com/fast-ai-nlp/amazon_review_polarity_csv.tgz",
-}
-
-
-class AmazonPolarityConfig(datasets.BuilderConfig):
-    """BuilderConfig for AmazonPolarity."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for AmazonPolarity.
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(AmazonPolarityConfig, self).__init__(**kwargs)
-
-
-class AmazonPolarity(datasets.GeneratorBasedBuilder):
-    """Amazon Polarity Classification Dataset."""
-
-    VERSION = datasets.Version("3.0.0")
-
-    BUILDER_CONFIGS = [
-        AmazonPolarityConfig(
-            name="amazon_polarity", version=VERSION, description="Amazon Polarity Classification Dataset."
-        ),
-    ]
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "label": datasets.features.ClassLabel(
-                    names=[
-                        "negative",
-                        "positive",
-                    ]
-                ),
-                "title": datasets.Value("string"),
-                "content": 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]
-        archive = dl_manager.download(my_urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": "/".join(["amazon_review_polarity_csv", "train.csv"]),
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": "/".join(["amazon_review_polarity_csv", "test.csv"]),
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, files):
-        """Yields examples."""
-        for path, f in files:
-            if path == filepath:
-                lines = (line.decode("utf-8") for line in f)
-                data = csv.reader(lines, delimiter=",", quoting=csv.QUOTE_ALL)
-                for id_, row in enumerate(data):
-                    yield id_, {
-                        "title": row[1],
-                        "content": row[2],
-                        "label": int(row[0]) - 1,
-                    }
-                break

amazon_polarity/test-00000-of-00001.parquet

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amazon_polarity/train-00000-of-00004.parquet

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amazon_polarity/train-00001-of-00004.parquet

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amazon_polarity/train-00002-of-00004.parquet

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amazon_polarity/train-00003-of-00004.parquet

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+size 254410930

dataset_infos.json

@@ -1 +0,0 @@
-{"amazon_polarity": {"description": "The Amazon reviews dataset consists of reviews from amazon.\nThe data span a period of 18 years, including ~35 million reviews up to March 2013.\nReviews include product and user information, ratings, and a plaintext review.\n", "citation": "@inproceedings{mcauley2013hidden,\n  title={Hidden factors and hidden topics: understanding rating dimensions with review text},\n  author={McAuley, Julian and Leskovec, Jure},\n  booktitle={Proceedings of the 7th ACM conference on Recommender systems},\n  pages={165--172},\n  year={2013}\n}\n", "homepage": "https://registry.opendata.aws/", "license": "Apache License 2.0", "features": {"label": {"num_classes": 2, "names": ["negative", "positive"], "id": null, "_type": "ClassLabel"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "content": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "amazon_polarity", "config_name": "amazon_polarity", "version": {"version_str": "3.0.0", "description": null, "major": 3, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1604364432, "num_examples": 3600000, "dataset_name": "amazon_polarity"}, "test": {"name": "test", "num_bytes": 178176193, "num_examples": 400000, "dataset_name": "amazon_polarity"}}, "download_checksums": {"https://s3.amazonaws.com/fast-ai-nlp/amazon_review_polarity_csv.tgz": {"num_bytes": 688339454, "checksum": "d2a3ee7a214497a5d1b8eaed7c8d7ba2737de00ada3b0ec46243983efa100361"}}, "download_size": 688339454, "post_processing_size": null, "dataset_size": 1782540625, "size_in_bytes": 2470880079}}