Initial commit

.gitattributes

@@ -52,3 +52,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+test.csv filter=lfs diff=lfs merge=lfs -text
+train.csv filter=lfs diff=lfs merge=lfs -text

imdb_pt.py

@@ -0,0 +1,86 @@
+"""IMDB movie reviews dataset translated to Portuguese."""
+
+import csv
+
+import datasets
+from datasets.tasks import TextClassification
+
+_DESCRIPTION = """\
+Large Movie Review Dataset.
+This is a dataset for binary sentiment classification containing substantially \
+more data than previous benchmark datasets. We provide a set of 25,000 highly \
+polar movie reviews for training, and 25,000 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/maritaca-ai/imdb_pt/resolve/main/"
+
+class IMDBReviewsConfig(datasets.BuilderConfig):
+    """BuilderConfig for IMDBReviews."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for IMDBReviews.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+
+class Imdb(datasets.GeneratorBasedBuilder):
+    """IMDB movie reviews dataset translated to Portuguese."""
+
+    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=["negativo", "posisivo"])}
+            ),
+            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):
+        train_path = dl_manager.download_and_extract(f"{_DOWNLOAD_URL}/train.csv")
+        test_path = dl_manager.download_and_extract(f"{_DOWNLOAD_URL}/test.csv")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path, "split": "train"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"filepath": test_path, "split": "test"}
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """Generate aclImdb examples."""
+        with open(filepath, encoding="utf-8") as csv_file:
+          csv_reader = csv.reader(
+              csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
+          )
+          for id_, row in enumerate(csv_reader):
+                  if id_ == 0:
+                    continue
+                  text, label = row
+                  yield id_, {"text": text, "label": label}

test.csv

@@ -0,0 +1,3 @@
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+oid sha256:67a549c2f0d5f9f06db7c59c43728f224a707ddb2e93698eb6bb99c23a81bb7f
+size 32799465

train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2fcdc5f90812858d4bd9ce8d3107583c60fcdef6ab071f649058c016e2c2966d
+size 33342600