Convert dataset to Parquet (#3)

- Convert dataset to Parquet (23c9800e1e347d2f770cd0afc7ea7a406afcf2b6)
- Delete loading script (0bbdc66575818bc89464f284017ffc6ed3f8f984)


Co-authored-by: Albert Villanova <albertvillanova@users.noreply.huggingface.co>

README.md

@@ -30,10 +30,15 @@ dataset_info:
           '1': negative
   splits:
   - name: train
-    num_bytes: 114670811
+    num_bytes: 114670791
     num_examples: 50000
-  download_size: 31510992
-  dataset_size: 114670811
+  download_size: 56234303
+  dataset_size: 114670791
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
 ---
 
 # Dataset Card for ImDB Urdu Reviews

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df742bf617460342a5ef9691e66be05b4ce9787cc3a12d6875d53b8c24e515a0
+size 56234303

imdb_urdu_reviews.py

@@ -1,73 +0,0 @@
-"""IMDB Urdu movie reviews dataset."""
-
-
-import csv
-import os
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_CITATION = """
-@InProceedings{maas-EtAl:2011:ACL-HLT2011,
-  author    = {Maas, Andrew L. and Daly,nRaymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y...},
-  title     = {Learning Word Vectors for Sentiment Analysis},
-  month     = {June},
-  year      = {2011},
-  address   = {Portland, Oregon, USA},
-  publisher = {Association for Computational Linguistics},
-  pages     = {142--150},
-  url       = {http://www.aclweb.org/anthology/P11-1015}
-}
-"""
-
-_DESCRIPTION = """
-Large Movie translated Urdu Reviews Dataset.
-This is a dataset for binary sentiment classification containing substantially more data than previous
-benchmark datasets. We provide a set of 40,000 highly polar movie reviews for training, and 10,000 for testing.
-To increase the availability of sentiment analysis dataset for a low recourse language like Urdu,
-we opted to use the already available IMDB Dataset. we have translated this dataset using google translator.
-This is a binary classification dataset having two classes as positive and negative.
-The reason behind using this dataset is high polarity for each class.
-It contains 50k samples equally divided in two classes.
-"""
-
-_URL = "https://github.com/mirfan899/Urdu/blob/master/sentiment/imdb_urdu_reviews.csv.tar.gz?raw=true"
-
-_HOMEPAGE = "https://github.com/mirfan899/Urdu"
-
-
-class ImdbUrduReviews(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "sentiment": datasets.ClassLabel(names=["positive", "negative"]),
-                }
-            ),
-            citation=_CITATION,
-            homepage=_HOMEPAGE,
-            task_templates=[TextClassification(text_column="sentence", label_column="sentiment")],
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        dl_path = dl_manager.download_and_extract(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(dl_path, "imdb_urdu_reviews.csv")}
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        with open(filepath, encoding="utf-8") as f:
-            reader = csv.reader(f, delimiter=",")
-            for id_, row in enumerate(reader):
-                if id_ == 0:
-                    continue
-                yield id_, {"sentiment": row[1], "sentence": row[0]}