Convert dataset to Parquet (#9)

- Convert dataset to Parquet (9ba0287246fae66ad81fd558408e8c411ca8d7cd)
- Delete loading script (7a61fcda05758130189b7c56464f3d9c41aef30f)

README.md

@@ -1,13 +1,14 @@
 ---
 language:
 - en
-pretty_name: YelpPolarity
 task_categories:
 - text-classification
 task_ids:
 - sentiment-classification
 paperswithcode_id: yelp-review-polarity
+pretty_name: YelpPolarity
 dataset_info:
+  config_name: plain_text
   features:
   - name: text
     dtype: string
@@ -17,7 +18,6 @@ dataset_info:
         names:
           '0': '1'
           '1': '2'
-  config_name: plain_text
   splits:
   - name: train
     num_bytes: 413558837
@@ -25,8 +25,16 @@ dataset_info:
   - name: test
     num_bytes: 27962097
     num_examples: 38000
-  download_size: 166373201
+  download_size: 273803261
   dataset_size: 441520934
+configs:
+- config_name: plain_text
+  data_files:
+  - split: train
+    path: plain_text/train-*
+  - split: test
+    path: plain_text/test-*
+  default: true
 train-eval-index:
 - config: plain_text
   task: text-classification

plain_text/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:43f89639c05b9a91634c8c972979e074942f59df15f6369ccb81a7f402af1a25
+size 17701458

plain_text/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87bbf1eee0dc21f1a2790f31d8e9fecb66d09b5ea54e8f246779597170c7c89b
+size 256101803

yelp_polarity.py

@@ -1,162 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# 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.
-
-# Lint as: python3
-# Copyright 2019 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# 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.
-"""Yelp Polarity Reviews dataset."""
-
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_DESCRIPTION = """\
-Large Yelp Review Dataset.
-This is a dataset for binary sentiment classification. \
-We provide a set of 560,000 highly polar yelp reviews for training, and 38,000 for testing. \
-
-ORIGIN
-The Yelp reviews dataset consists of reviews from Yelp. It is extracted
-from the Yelp Dataset Challenge 2015 data. For more information, please
-refer to http://www.yelp.com/dataset_challenge
-
-The Yelp reviews polarity dataset is constructed by
-Xiang Zhang ([email protected]) from the above dataset.
-It is first used as a text classification benchmark in the following paper:
-Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks
-for Text Classification. Advances in Neural Information Processing Systems 28
-(NIPS 2015).
-
-
-DESCRIPTION
-
-The Yelp reviews polarity dataset is constructed by considering stars 1 and 2
-negative, and 3 and 4 positive. For each polarity 280,000 training samples and
-19,000 testing samples are take randomly. In total there are 560,000 trainig
-samples and 38,000 testing samples. Negative polarity is class 1,
-and positive class 2.
-
-The files train.csv and test.csv contain all the training samples as
-comma-sparated values. There are 2 columns in them, corresponding to class
-index (1 and 2) and review text. The review texts are escaped using double
-quotes ("), and any internal double quote is escaped by 2 double quotes ("").
-New lines are escaped by a backslash followed with an "n" character,
-that is "\n".
-"""
-
-_CITATION = """\
-@article{zhangCharacterlevelConvolutionalNetworks2015,
-  archivePrefix = {arXiv},
-  eprinttype = {arxiv},
-  eprint = {1509.01626},
-  primaryClass = {cs},
-  title = {Character-Level {{Convolutional Networks}} for {{Text Classification}}},
-  abstract = {This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.},
-  journal = {arXiv:1509.01626 [cs]},
-  author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann},
-  month = sep,
-  year = {2015},
-}
-
-"""
-
-_DOWNLOAD_URL = "https://s3.amazonaws.com/fast-ai-nlp/yelp_review_polarity_csv.tgz"
-
-
-class YelpPolarityReviewsConfig(datasets.BuilderConfig):
-    """BuilderConfig for YelpPolarityReviews."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for YelpPolarityReviews.
-
-        Args:
-
-            **kwargs: keyword arguments forwarded to super.
-        """
-        super(YelpPolarityReviewsConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
-
-
-class YelpPolarity(datasets.GeneratorBasedBuilder):
-    """Yelp Polarity reviews dataset."""
-
-    BUILDER_CONFIGS = [
-        YelpPolarityReviewsConfig(
-            name="plain_text",
-            description="Plain text",
-        )
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "text": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(names=["1", "2"]),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://course.fast.ai/datasets",
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="text", label_column="label")],
-        )
-
-    def _vocab_text_gen(self, train_file):
-        for _, ex in self._generate_examples(train_file):
-            yield ex["text"]
-
-    def _split_generators(self, dl_manager):
-        arch_path = dl_manager.download(_DOWNLOAD_URL)
-        train_file = "yelp_review_polarity_csv/train.csv"
-        test_file = "yelp_review_polarity_csv/test.csv"
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": train_file,
-                    "files": dl_manager.iter_archive(arch_path),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": test_file,
-                    "files": dl_manager.iter_archive(arch_path),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, files):
-        """Generate Yelp examples."""
-        for path, f in files:
-            if path == filepath:
-                for line_id, line in enumerate(f):
-                    line = line.decode("utf-8")
-                    # The format of the line is:
-                    # "1", "The text of the review."
-                    yield line_id, {"text": line[5:-2].strip(), "label": line[1]}
-                break