Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- crowdsourced
+- found
+language_creators:
+- crowdsourced
+- found
+languages:
+- en
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- extended|other-nus-sms-corpus
+task_categories:
+- text-classification
+task_ids:
+- intent-classification
+---
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** http://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection
+- **Repository:**
+- **Paper:** Almeida, T.A., Gomez Hidalgo, J.M., Yamakami, A. Contributions to the study of SMS Spam Filtering: New Collection and Results. Proceedings of the 2011 ACM Symposium on Document Engineering (ACM DOCENG'11), Mountain View, CA, USA, 2011.
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+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.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+- sms: the sms message
+- label: indicating if the sms message is ham or spam, ham means it is not spam
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+@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)",
+}

dataset_infos.json

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+{"plain_text": {"description": "The SMS Spam Collection v.1 is a public set of SMS labeled messages that have been collected for mobile phone spam research. \nIt has one collection composed by 5,574 English, real and non-enconded messages, tagged according being legitimate (ham) or spam.\n", "citation": "@inproceedings{Almeida2011SpamFiltering,\n  title={Contributions to the Study of SMS Spam Filtering: New Collection and Results},\n  author={Tiago A. Almeida and Jose Maria Gomez Hidalgo and Akebo Yamakami},\n  year={2011},\n  booktitle = \"Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG'11)\",\n}\n", "homepage": "http://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection", "license": "", "features": {"sms": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["ham", "spam"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "sms_spam", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 521756, "num_examples": 5574, "dataset_name": "sms_spam"}}, "download_checksums": {"http://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip": {"num_bytes": 203415, "checksum": "1587ea43e58e82b14ff1f5425c88e17f8496bfcdb67a583dbff9eefaf9963ce3"}}, "download_size": 203415, "post_processing_size": null, "dataset_size": 521756, "size_in_bytes": 725171}}

dummy/plain_text/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:105b0e273e0da2af4b3dd786241ae4eb12bf36d1c41f67f7f82dd41e305ff77a
+size 733

sms_spam.py

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+# 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"""
+
+from __future__ import absolute_import, division, print_function
+
+import os
+
+import datasets
+
+
+_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,
+        )
+
+    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,
+                }