Convert dataset to Parquet (#4)

- Convert dataset to Parquet (c671344ae6d7bb6a45d5745a503e4a5baebc4be8)
- Delete loading script (bdd45d206343823a016721e750044ce246b171ef)

README.md

@@ -24,6 +24,7 @@ task_ids:
 paperswithcode_id: dbrd
 pretty_name: DBRD
 dataset_info:
+  config_name: plain_text
   features:
   - name: text
     dtype: string
@@ -33,19 +34,28 @@ dataset_info:
         names:
           '0': neg
           '1': pos
-  config_name: plain_text
   splits:
   - name: train
-    num_bytes: 29496333
+    num_bytes: 29496321
     num_examples: 20028
   - name: test
-    num_bytes: 3246243
+    num_bytes: 3246239
     num_examples: 2224
   - name: unsupervised
-    num_bytes: 152733031
+    num_bytes: 152732991
     num_examples: 96264
-  download_size: 79065872
-  dataset_size: 185475607
+  download_size: 116489037
+  dataset_size: 185475551
+configs:
+- config_name: plain_text
+  data_files:
+  - split: train
+    path: plain_text/train-*
+  - split: test
+    path: plain_text/test-*
+  - split: unsupervised
+    path: plain_text/unsupervised-*
+  default: true
 ---
 
 # Dataset Card for DBRD

dbrd.py

@@ -1,112 +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
-"""Dutch Book Review Dataset"""
-
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_DESCRIPTION = """\
-The Dutch Book Review Dataset (DBRD) contains over 110k book reviews of which \
-22k have associated binary sentiment polarity labels. It is intended as a \
-benchmark for sentiment classification in Dutch and created due to a lack of \
-annotated datasets in Dutch that are suitable for this task.
-"""
-
-_CITATION = """\
-@article{DBLP:journals/corr/abs-1910-00896,
-  author    = {Benjamin van der Burgh and
-               Suzan Verberne},
-  title     = {The merits of Universal Language Model Fine-tuning for Small Datasets
-               - a case with Dutch book reviews},
-  journal   = {CoRR},
-  volume    = {abs/1910.00896},
-  year      = {2019},
-  url       = {http://arxiv.org/abs/1910.00896},
-  archivePrefix = {arXiv},
-  eprint    = {1910.00896},
-  timestamp = {Fri, 04 Oct 2019 12:28:06 +0200},
-  biburl    = {https://dblp.org/rec/journals/corr/abs-1910-00896.bib},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
-}
-"""
-
-_DOWNLOAD_URL = "https://github.com/benjaminvdb/DBRD/releases/download/v3.0/DBRD_v3.tgz"
-
-
-class DBRDConfig(datasets.BuilderConfig):
-    """BuilderConfig for DBRD."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for DBRD.
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(DBRDConfig, self).__init__(version=datasets.Version("3.0.0", ""), **kwargs)
-
-
-class DBRD(datasets.GeneratorBasedBuilder):
-    """Dutch Book Review Dataset."""
-
-    BUILDER_CONFIGS = [
-        DBRDConfig(
-            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=["neg", "pos"])}
-            ),
-            supervised_keys=None,
-            homepage="https://github.com/benjaminvdb/DBRD",
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="text", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        archive = dl_manager.download(_DOWNLOAD_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "train"}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "test"}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split("unsupervised"),
-                gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "unsup", "labeled": False},
-            ),
-        ]
-
-    def _generate_examples(self, files, split, labeled=True):
-        """Generate DBRD examples."""
-        # For labeled examples, extract the label from the path.
-        if labeled:
-            for path, f in files:
-                if path.startswith(f"DBRD/{split}"):
-                    label = {"pos": 1, "neg": 0}[path.split("/")[2]]
-                    yield path, {"text": f.read().decode("utf-8"), "label": label}
-        else:
-            for path, f in files:
-                if path.startswith(f"DBRD/{split}"):
-                    yield path, {"text": f.read().decode("utf-8"), "label": -1}

plain_text/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+oid sha256:28ec1d27ebcbdbd17b0724baa54afcb74c365d2bb0f08f05e3f21c249a5e8d8a
+size 2057593

plain_text/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+oid sha256:61d360f0db2475f5e03070377347d41350579a8034fc6fda9d1999cacb9e313f
+size 18558956

plain_text/unsupervised-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+oid sha256:3865799b9b2a30c21a15d631106f6e3955c3040ed19288bd85c0d98f4b0b9eaf
+size 95872488