Add dataset script

imdb_dutch.py

@@ -0,0 +1,127 @@
+# 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
+"""IMDB movie reviews dataset."""
+import gzip
+import json
+
+import datasets
+from datasets.tasks import TextClassification
+
+
+_DESCRIPTION = """\
+Large Movie Review Dataset translated to Dutch.
+
+This is a dataset for binary sentiment classification containing substantially \
+more data than previous benchmark datasets. We provide a set of 24,992 highly \
+polar movie reviews for training, and 24,992 for testing. There is additional \
+unlabeled data for use as well.\
+"""
+
+_CITATION = """\
+@InProceedings{maas-EtAl:2011:ACL-HLT2011,
+  author    = {Maas, Andrew L.  and  Daly, Raymond E.  and  Pham, Peter T.  and  Huang, Dan  and  Ng, Andrew Y.  and  Potts, Christopher},
+  title     = {Learning Word Vectors for Sentiment Analysis},
+  booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
+  month     = {June},
+  year      = {2011},
+  address   = {Portland, Oregon, USA},
+  publisher = {Association for Computational Linguistics},
+  pages     = {142--150},
+  url       = {http://www.aclweb.org/anthology/P11-1015}
+}
+"""
+
+_DOWNLOAD_URL = "https://huggingface.co/datasets/yhavinga/imdb_dutch/resolve/main/dataset/{split}.gz"
+
+
+class IMDBReviewsConfig(datasets.BuilderConfig):
+    """BuilderConfig for IMDBReviews."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for IMDBReviews.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(IMDBReviewsConfig, self).__init__(
+            version=datasets.Version("1.0.0", ""), **kwargs
+        )
+
+
+class Imdb(datasets.GeneratorBasedBuilder):
+    """IMDB movie reviews dataset."""
+
+    BUILDER_CONFIGS = [
+        IMDBReviewsConfig(
+            name="plain_text",
+            description="Plain text",
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "text_en": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=["neg", "pos"]),
+                }
+            ),
+            supervised_keys=None,
+            homepage="http://ai.stanford.edu/~amaas/data/sentiment/",
+            citation=_CITATION,
+            task_templates=[
+                TextClassification(text_column="text", label_column="label")
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "files": dl_manager.download(_DOWNLOAD_URL.format(split="train")),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "files": dl_manager.download(_DOWNLOAD_URL.format(split="test")),
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split("unsupervised"),
+                gen_kwargs={
+                    "files": dl_manager.download(
+                        _DOWNLOAD_URL.format(split="unsupervised")
+                    ),
+                    "split": "unsupervised",
+                    "labeled": False,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, files, split, labeled=True):
+        """Generate aclImdb examples."""
+        for filepath in files:
+            with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
+                for _, line in enumerate(f):
+                    example = json.loads(line)
+                    yield _, example