Initial commit

.gitattributes

@@ -52,3 +52,4 @@ 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
+*.csv filter=lfs diff=lfs merge=lfs -text

sst2_pt.py

@@ -0,0 +1,65 @@
+"""IMDB movie reviews dataset translated to Portuguese."""
+
+import csv
+
+import datasets
+from datasets.tasks import TextClassification
+
+_DESCRIPTION = """\
+The Stanford Sentiment Treebank consists of sentences from movie reviews and
+human annotations of their sentiment. The task is to predict the sentiment of a
+given sentence. We use the two-way (positive/negative) class split, and use only
+sentence-level labels.
+"""
+
+_CITATION = """\
+@inproceedings{socher2013recursive,
+  title={Recursive deep models for semantic compositionality over a sentiment treebank},
+  author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew and Potts, Christopher},
+  booktitle={Proceedings of the 2013 conference on empirical methods in natural language processing},
+  pages={1631--1642},
+  year={2013}
+}
+"""
+
+_HOMEPAGE = "https://nlp.stanford.edu/sentiment/"
+
+_DOWNLOAD_URL = "https://huggingface.co/datasets/maritaca-ai/sst2_pt/resolve/main"
+
+class Imdb(datasets.GeneratorBasedBuilder):
+    """The Stanford Sentiment Treebank to Portuguese."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["negativo", "positivo"])}
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            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):
+        with open(filepath, encoding="utf-8") as csv_file:
+          csv_reader = csv.reader(
+              csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
+          )
+          for row in csv_reader:
+                  if id_ == 0:
+                    continue
+                  idx, text, label = row
+                  yield idx, {"text": text, "label": label}

train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:71cced99a8182ce47e314b20eb91b843ca24b95fa0b96aa723a59f4778c7cbaf
+size 4406612

validation.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:30b859576d8f5e60283669869344c7ee1db64ccec80b28dd76abbd246c727b91
+size 103379