Convert dataset to Parquet (#4)

- Convert dataset to Parquet (6b67ee6459ee3a2020f145f85861c2501ae9b05a)
- Delete loading script (dafc90c40ebd1c01c0a17d48c2e3ccf6c8536150)

README.md

@@ -22,6 +22,7 @@ task_ids:
 paperswithcode_id: sms-spam-collection-data-set
 pretty_name: SMS Spam Collection Data Set
 dataset_info:
+  config_name: plain_text
   features:
   - name: sms
     dtype: string
@@ -31,13 +32,18 @@ dataset_info:
         names:
           '0': ham
           '1': spam
-  config_name: plain_text
   splits:
   - name: train
-    num_bytes: 521756
+    num_bytes: 521752
     num_examples: 5574
-  download_size: 203415
-  dataset_size: 521756
+  download_size: 358869
+  dataset_size: 521752
+configs:
+- config_name: plain_text
+  data_files:
+  - split: train
+    path: plain_text/train-*
+  default: true
 train-eval-index:
 - config: plain_text
   task: text-classification

plain_text/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6e5518e4a49cb2de8af9c89a38b742825cdddbb55942701fc2237d4364288abd
+size 358869

sms_spam.py

@@ -1,92 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# 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.
-
-# Lint as: python3
-"""SMS Spam Collection Data Set"""
-
-
-import os
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_CITATION = """\
-@inproceedings{Almeida2011SpamFiltering,
-  title={Contributions to the Study of SMS Spam Filtering: New Collection and Results},
-  author={Tiago A. Almeida and Jose Maria Gomez Hidalgo and Akebo Yamakami},
-  year={2011},
-  booktitle = "Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG'11)",
-}
-"""
-
-_DESCRIPTION = """\
-The SMS Spam Collection v.1 is a public set of SMS labeled messages that have been collected for mobile phone spam research.
-It has one collection composed by 5,574 English, real and non-enconded messages, tagged according being legitimate (ham) or spam.
-"""
-
-_DATA_URL = "http://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip"
-
-
-class SmsSpam(datasets.GeneratorBasedBuilder):
-    """SMS Spam Collection Data Set"""
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="plain_text",
-            version=datasets.Version("1.0.0", ""),
-            description="Plain text import of SMS Spam Collection Data Set",
-        )
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "sms": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(names=["ham", "spam"]),
-                }
-            ),
-            supervised_keys=("sms", "label"),
-            homepage="http://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection",
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="sms", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        dl_dir = dl_manager.download_and_extract(_DATA_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(dl_dir, "SMSSpamCollection")}
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-
-        with open(filepath, encoding="utf-8") as sms_file:
-            for idx, line in enumerate(sms_file):
-                fields = line.split("\t")
-
-                if fields[0] == "ham":
-                    label = 0
-                else:
-                    label = 1
-
-                yield idx, {
-                    "sms": fields[1],
-                    "label": label,
-                }