Convert dataset to Parquet (#7)

- Convert dataset to Parquet (f817a2b0170bf1822262d6198427436189c07d04)
- Delete loading script (283dc807270f854f56ed56a1d5ef27109ef8c917)
- Delete legacy dataset_infos.json (703426cda46e0d65db9fe8143397ecee1ec16cc0)

README.md

@@ -33,16 +33,25 @@ dataset_info:
           '1': positive
   splits:
   - name: train
-    num_bytes: 4690022
+    num_bytes: 4681603
     num_examples: 67349
   - name: validation
-    num_bytes: 106361
+    num_bytes: 106252
     num_examples: 872
   - name: test
-    num_bytes: 216868
+    num_bytes: 216640
     num_examples: 1821
-  download_size: 7439277
-  dataset_size: 5013251
+  download_size: 3331058
+  dataset_size: 5004495
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+  - split: test
+    path: data/test-*
 ---
 
 # Dataset Card for [Dataset Name]

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:20d27a86c0c59acb746a41a481ebb1fc71edb72d94b5ccee7f23b9041b17adcf
+size 147787

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c7921283b75a42e685f50edecb96798607ea0fcbfd0739ee8975f22c12d55f09
+size 3110458

data/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fb00fe008f6828f86ba2beda8415a4cf5da0c884f21c5f238c87131b5aa19529
+size 72813

dataset_infos.json

@@ -1 +0,0 @@
-{"default": {"description": "The Stanford Sentiment Treebank consists of sentences from movie reviews and\nhuman annotations of their sentiment. The task is to predict the sentiment of a\ngiven sentence. We use the two-way (positive/negative) class split, and use only\nsentence-level labels.\n", "citation": "@inproceedings{socher2013recursive,\n  title={Recursive deep models for semantic compositionality over a sentiment treebank},\n  author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew and Potts, Christopher},\n  booktitle={Proceedings of the 2013 conference on empirical methods in natural language processing},\n  pages={1631--1642},\n  year={2013}\n}\n", "homepage": "https://nlp.stanford.edu/sentiment/", "license": "Unknown", "features": {"idx": {"dtype": "int32", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["negative", "positive"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "sst2", "config_name": "default", "version": {"version_str": "2.0.0", "description": null, "major": 2, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4690022, "num_examples": 67349, "dataset_name": "sst2"}, "validation": {"name": "validation", "num_bytes": 106361, "num_examples": 872, "dataset_name": "sst2"}, "test": {"name": "test", "num_bytes": 216868, "num_examples": 1821, "dataset_name": "sst2"}}, "download_checksums": {"https://dl.fbaipublicfiles.com/glue/data/SST-2.zip": {"num_bytes": 7439277, "checksum": "d67e16fb55739c1b32cdce9877596db1c127dc322d93c082281f64057c16deaa"}}, "download_size": 7439277, "post_processing_size": null, "dataset_size": 5013251, "size_in_bytes": 12452528}}

sst2.py

@@ -1,105 +0,0 @@
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""SST-2 (Stanford Sentiment Treebank v2) dataset."""
-
-
-import csv
-import os
-
-import datasets
-
-
-_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}
-}
-"""
-
-_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.
-"""
-
-_HOMEPAGE = "https://nlp.stanford.edu/sentiment/"
-
-_LICENSE = "Unknown"
-
-_URL = "https://dl.fbaipublicfiles.com/glue/data/SST-2.zip"
-
-
-class Sst2(datasets.GeneratorBasedBuilder):
-    """SST-2 dataset."""
-
-    VERSION = datasets.Version("2.0.0")
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "idx": datasets.Value("int32"),
-                "sentence": datasets.Value("string"),
-                "label": datasets.features.ClassLabel(names=["negative", "positive"]),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        dl_dir = dl_manager.download_and_extract(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "file_paths": dl_manager.iter_files(dl_dir),
-                    "data_filename": "train.tsv",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "file_paths": dl_manager.iter_files(dl_dir),
-                    "data_filename": "dev.tsv",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "file_paths": dl_manager.iter_files(dl_dir),
-                    "data_filename": "test.tsv",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, file_paths, data_filename):
-        for file_path in file_paths:
-            filename = os.path.basename(file_path)
-            if filename == data_filename:
-                with open(file_path, encoding="utf8") as f:
-                    reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
-                    for idx, row in enumerate(reader):
-                        yield idx, {
-                            "idx": row["index"] if "index" in row else idx,
-                            "sentence": row["sentence"],
-                            "label": int(row["label"]) if "label" in row else -1,
-                        }