Upload rotten_tomatoes_reviews.py

rotten_tomatoes_reviews.py

@@ -0,0 +1,114 @@
+import datasets
+from datasets.tasks import TextClassification
+
+_DESCRIPTION = """
+Movie Review Dataset.
+
+This is a dataset containing 4,265 positive and 4,265 negative processed
+sentences from Rotten Tomatoes movie reviews.
+"""
+
+_CITATION = """
+@InProceedings{Pang+Lee:05a,
+  author =       {Bo Pang and Lillian Lee},
+  title =        {Seeing stars: Exploiting class relationships for sentiment
+                  categorization with respect to rating scales},
+  booktitle =    {Proceedings of the ACL},
+  year =         2005
+}
+"""
+
+_DOWNLOAD_URL = "https://testerstories/files/ai_learn/rt-polaritydata.tar.gz"
+
+
+class RottenTomatoesReviews(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=["neg", "pos"]),
+                }
+            ),
+            supervised_keys=[""],
+            homepage="http://www.cs.cornell.edu/people/pabo/movie-review-data/",
+            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={
+                    "split_key": "train",
+                    "files": dl_manager.iter_archive(archive),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "split_key": "validation",
+                    "files": dl_manager.iter_archive(archive),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "split_key": "test",
+                    "files": dl_manager.iter_archive(archive),
+                },
+            ),
+        ]
+
+    def _get_examples_from_split(self, split_key, files):
+        data_dir = "rt-polaritydata/"
+        pos_samples, neg_samples = None, None
+
+        for path, f in files:
+            if path == data_dir + "rt-polarity.pos":
+                pos_samples = [line.decode("latin-1").strip() for line in f]
+            elif path == data_dir + "rt-polarity.neg":
+                neg_samples = [line.decode("latin-1").strip() for line in f]
+
+            if pos_samples is not None and neg_samples is not None:
+                break
+
+        i1 = int(len(pos_samples) * 0.8 + 0.5)
+        i2 = int(len(pos_samples) * 0.9 + 0.5)
+
+        train_samples = pos_samples[:i1] + neg_samples[:i1]
+        train_labels = (["pos"] * i1) + (["neg"] * i1)
+
+        validation_samples = pos_samples[i1:i2] + neg_samples[i1:i2]
+        validation_labels = (["pos"] * (i2 - i1)) + (["neg"] * (i2 - i1))
+
+        test_samples = pos_samples[i2:] + neg_samples[i2:]
+        test_labels = (["pos"] * (len(pos_samples) - i2)) + (
+            ["neg"] * (len(pos_samples) - i2)
+        )
+
+        if split_key == "train":
+            return (train_samples, train_labels)
+        if split_key == "validation":
+            return (validation_samples, validation_labels)
+        if split_key == "test":
+            return (test_samples, test_labels)
+        else:
+            raise ValueError(f"Invalid split key {split_key}")
+
+    def _generate_examples(self, split_key, files):
+        split_text, split_labels = self._get_examples_from_split(split_key, files)
+
+        for text, label in zip(split_text, split_labels):
+            data_key = split_key + "_" + text
+            feature_dict = {"text": text, "label": label}
+
+            yield data_key, feature_dict