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tesis.py

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+# 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 datasets
+from datasets.tasks import TextClassification
+
+
+_DESCRIPTION = """
+"""
+
+_CITATION = """
+"""
+
+_DOWNLOAD_URL = "aclImdb_v1.tar.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"), "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):
+        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": "train", "labeled": False},
+            ),
+        ]
+
+    def _generate_examples(self, files, split, labeled=True):
+        """Generate aclImdb examples."""
+        # For labeled examples, extract the label from the path.
+        if labeled:
+            label_mapping = {"pos": 1, "neg": 0}
+            for path, f in files:
+                if path.startswith(f"aclImdb/{split}"):
+                    label = label_mapping.get(path.split("/")[2])
+                    if label is not None:
+                        yield path, {"text": f.read().decode("utf-8"), "label": label}
+        else:
+            for path, f in files:
+                if path.startswith(f"aclImdb/{split}"):
+                    if path.split("/")[2] == "unsup":
+                        yield path, {"text": f.read().decode("utf-8"), "label": -1}