Convert dataset to Parquet

README.md

@@ -20,6 +20,7 @@ task_ids:
 paperswithcode_id: allocine
 pretty_name: Allociné
 dataset_info:
+  config_name: allocine
   features:
   - name: review
     dtype: string
@@ -29,19 +30,28 @@ dataset_info:
         names:
           '0': neg
           '1': pos
-  config_name: allocine
   splits:
   - name: train
-    num_bytes: 91330696
+    num_bytes: 91330632
     num_examples: 160000
   - name: validation
-    num_bytes: 11546250
+    num_bytes: 11546242
     num_examples: 20000
   - name: test
-    num_bytes: 11547697
+    num_bytes: 11547689
     num_examples: 20000
-  download_size: 66625305
-  dataset_size: 114424643
+  download_size: 75125954
+  dataset_size: 114424563
+configs:
+- config_name: allocine
+  data_files:
+  - split: train
+    path: allocine/train-*
+  - split: validation
+    path: allocine/validation-*
+  - split: test
+    path: allocine/test-*
+  default: true
 train-eval-index:
 - config: allocine
   task: text-classification

allocine.py

@@ -1,106 +0,0 @@
-"""Allocine Dataset: A Large-Scale French Movie Reviews Dataset."""
-
-
-import json
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_CITATION = """\
-@misc{blard2019allocine,
-  author = {Blard, Theophile},
-  title = {french-sentiment-analysis-with-bert},
-  year = {2020},
-  publisher = {GitHub},
-  journal = {GitHub repository},
-  howpublished={\\url{https://github.com/TheophileBlard/french-sentiment-analysis-with-bert}},
-}
-"""
-
-_DESCRIPTION = """\
- Allocine Dataset: A Large-Scale French Movie Reviews Dataset.
- This is a dataset for binary sentiment classification, made of user reviews scraped from Allocine.fr.
- It contains 100k positive and 100k negative reviews divided into 3 balanced splits: train (160k reviews), val (20k) and test (20k).
-"""
-
-
-class AllocineConfig(datasets.BuilderConfig):
-    """BuilderConfig for Allocine."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for Allocine.
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(AllocineConfig, self).__init__(**kwargs)
-
-
-class AllocineDataset(datasets.GeneratorBasedBuilder):
-    """Allocine Dataset: A Large-Scale French Movie Reviews Dataset."""
-
-    _DOWNLOAD_URL = "https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/raw/master/allocine_dataset/data.tar.bz2"
-    _TRAIN_FILE = "train.jsonl"
-    _VAL_FILE = "val.jsonl"
-    _TEST_FILE = "test.jsonl"
-
-    BUILDER_CONFIGS = [
-        AllocineConfig(
-            name="allocine",
-            version=datasets.Version("1.0.0"),
-            description="Allocine Dataset: A Large-Scale French Movie Reviews Dataset",
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "review": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(names=["neg", "pos"]),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://github.com/TheophileBlard/french-sentiment-analysis-with-bert",
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="review", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        archive_path = dl_manager.download(self._DOWNLOAD_URL)
-        data_dir = "data"
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": f"{data_dir}/{self._TRAIN_FILE}",
-                    "files": dl_manager.iter_archive(archive_path),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": f"{data_dir}/{self._VAL_FILE}",
-                    "files": dl_manager.iter_archive(archive_path),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": f"{data_dir}/{self._TEST_FILE}",
-                    "files": dl_manager.iter_archive(archive_path),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, files):
-        """Generate Allocine examples."""
-        for path, file in files:
-            if path == filepath:
-                for id_, row in enumerate(file):
-                    data = json.loads(row.decode("utf-8"))
-                    review = data["review"]
-                    label = "neg" if data["polarity"] == 0 else "pos"
-                    yield id_, {"review": review, "label": label}

allocine/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0e313c31e2db65eae40072f525eb0bc3567817baad70a85f798836b1e5be5a88
+size 7580549

allocine/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5cdabde7b62d2d56a2bc24e790cb9697057645103b607c281d82092bc5d53307
+size 59970147

allocine/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c2a25489c7f923475a11756071acc20df1a967b58042ca853698800164e731aa
+size 7575258

dataset_infos.json

@@ -1 +0,0 @@
-{"allocine": {"description": " Allocine Dataset: A Large-Scale French Movie Reviews Dataset.\n This is a dataset for binary sentiment classification, made of user reviews scraped from Allocine.fr.\n It contains 100k positive and 100k negative reviews divided into 3 balanced splits: train (160k reviews), val (20k) and test (20k).\n", "citation": "@misc{blard2019allocine,\n  author = {Blard, Theophile},\n  title = {french-sentiment-analysis-with-bert},\n  year = {2020},\n  publisher = {GitHub},\n  journal = {GitHub repository},\n  howpublished={\\url{https://github.com/TheophileBlard/french-sentiment-analysis-with-bert}},\n}\n", "homepage": "https://github.com/TheophileBlard/french-sentiment-analysis-with-bert", "license": "", "features": {"review": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["neg", "pos"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": [{"task": "text-classification", "text_column": "review", "label_column": "label", "labels": ["neg", "pos"]}], "builder_name": "allocine_dataset", "config_name": "allocine", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 91330696, "num_examples": 160000, "dataset_name": "allocine_dataset"}, "validation": {"name": "validation", "num_bytes": 11546250, "num_examples": 20000, "dataset_name": "allocine_dataset"}, "test": {"name": "test", "num_bytes": 11547697, "num_examples": 20000, "dataset_name": "allocine_dataset"}}, "download_checksums": {"https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/raw/master/allocine_dataset/data.tar.bz2": {"num_bytes": 66625305, "checksum": "8c49a8cac783da201697ed1a91b36d2f6618222b3b7ea1c2996f2a3fbc37dfb4"}}, "download_size": 66625305, "post_processing_size": null, "dataset_size": 114424643, "size_in_bytes": 181049948}}