Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
English
Size:
1M - 10M
ArXiv:
License:
Convert dataset to Parquet
#3
by
albertvillanova
HF staff
- opened
- README.md +10 -2
- amazon_polarity.py +0 -126
- amazon_polarity/test-00000-of-00001.parquet +3 -0
- amazon_polarity/train-00000-of-00004.parquet +3 -0
- amazon_polarity/train-00001-of-00004.parquet +3 -0
- amazon_polarity/train-00002-of-00004.parquet +3 -0
- amazon_polarity/train-00003-of-00004.parquet +3 -0
- dataset_infos.json +0 -1
README.md
CHANGED
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- sentiment-classification
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pretty_name: Amazon Review Polarity
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dataset_info:
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features:
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- name: label
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dtype:
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@@ -30,7 +31,6 @@ dataset_info:
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dtype: string
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- name: content
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dtype: string
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-
config_name: amazon_polarity
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splits:
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- name: train
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num_bytes: 1604364432
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- name: test
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num_bytes: 178176193
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num_examples: 400000
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-
download_size:
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dataset_size: 1782540625
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train-eval-index:
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- config: amazon_polarity
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task: text-classification
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- sentiment-classification
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pretty_name: Amazon Review Polarity
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dataset_info:
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+
config_name: amazon_polarity
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features:
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- name: label
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dtype:
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dtype: string
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- name: content
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dtype: string
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splits:
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- name: train
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num_bytes: 1604364432
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- name: test
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num_bytes: 178176193
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num_examples: 400000
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+
download_size: 1145430497
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dataset_size: 1782540625
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configs:
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- config_name: amazon_polarity
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data_files:
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- split: train
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path: amazon_polarity/train-*
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- split: test
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path: amazon_polarity/test-*
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default: true
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train-eval-index:
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- config: amazon_polarity
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task: text-classification
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amazon_polarity.py
DELETED
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-
# coding=utf-8
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""The amazon polarity dataset for text classification."""
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import csv
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import datasets
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_CITATION = """\
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@inproceedings{mcauley2013hidden,
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title={Hidden factors and hidden topics: understanding rating dimensions with review text},
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author={McAuley, Julian and Leskovec, Jure},
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booktitle={Proceedings of the 7th ACM conference on Recommender systems},
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pages={165--172},
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year={2013}
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}
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"""
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-
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_DESCRIPTION = """\
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-
The Amazon reviews dataset consists of reviews from amazon.
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The data span a period of 18 years, including ~35 million reviews up to March 2013.
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Reviews include product and user information, ratings, and a plaintext review.
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"""
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_HOMEPAGE = "https://registry.opendata.aws/"
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_LICENSE = "Apache License 2.0"
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_URLs = {
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"amazon_polarity": "https://s3.amazonaws.com/fast-ai-nlp/amazon_review_polarity_csv.tgz",
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}
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class AmazonPolarityConfig(datasets.BuilderConfig):
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"""BuilderConfig for AmazonPolarity."""
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def __init__(self, **kwargs):
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"""BuilderConfig for AmazonPolarity.
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Args:
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**kwargs: keyword arguments forwarded to super.
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"""
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super(AmazonPolarityConfig, self).__init__(**kwargs)
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class AmazonPolarity(datasets.GeneratorBasedBuilder):
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"""Amazon Polarity Classification Dataset."""
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VERSION = datasets.Version("3.0.0")
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BUILDER_CONFIGS = [
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AmazonPolarityConfig(
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name="amazon_polarity", version=VERSION, description="Amazon Polarity Classification Dataset."
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),
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]
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def _info(self):
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features = datasets.Features(
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{
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"label": datasets.features.ClassLabel(
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names=[
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"negative",
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"positive",
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]
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),
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"title": datasets.Value("string"),
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"content": datasets.Value("string"),
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}
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)
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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supervised_keys=None,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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my_urls = _URLs[self.config.name]
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archive = dl_manager.download(my_urls)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"filepath": "/".join(["amazon_review_polarity_csv", "train.csv"]),
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"files": dl_manager.iter_archive(archive),
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"filepath": "/".join(["amazon_review_polarity_csv", "test.csv"]),
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"files": dl_manager.iter_archive(archive),
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},
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),
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]
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def _generate_examples(self, filepath, files):
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"""Yields examples."""
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for path, f in files:
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if path == filepath:
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lines = (line.decode("utf-8") for line in f)
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data = csv.reader(lines, delimiter=",", quoting=csv.QUOTE_ALL)
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for id_, row in enumerate(data):
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yield id_, {
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"title": row[1],
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"content": row[2],
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"label": int(row[0]) - 1,
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}
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break
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amazon_polarity/test-00000-of-00001.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:65613cc6ec1ab30e19c4dcb8d8fa5612159d77bfa493704b4a9a9c167424992e
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size 117422360
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amazon_polarity/train-00000-of-00004.parquet
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:57c367f8c74210dde3742b17d103af33820df3af39d029f2a5051a6f87810661
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+
size 259761770
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amazon_polarity/train-00001-of-00004.parquet
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:14c7fee8f066fffc29eb906f80581836cca10218509a8ccf9898ff2a0c48f310
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size 258363554
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amazon_polarity/train-00002-of-00004.parquet
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+
version https://git-lfs.github.com/spec/v1
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+
oid sha256:5af7b024d3c4b759c1538b1987ece5bac47ef49ee98f6731fd2336caac283dc9
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+
size 255471883
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amazon_polarity/train-00003-of-00004.parquet
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+
version https://git-lfs.github.com/spec/v1
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+
oid sha256:04dcdf40f04b70dd213b910468049196c73e405decced4d292e10412bf248875
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+
size 254410930
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dataset_infos.json
DELETED
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-
{"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}}
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