Update files from the datasets library (from 1.2.0)

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

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README.md

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+---
+annotations_creators:
+- other
+language_creators:
+- other
+languages:
+- kn
+licenses:
+- cc-by-sa-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- topic-classification
+---
+
+# Dataset Card for kannada_news dataset 
+
+## 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:** [Kaggle link](https://www.kaggle.com/disisbig/kannada-news-dataset) for kannada news headlines dataset
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:** More information about the dataset and the models can be found [here](https://github.com/goru001/nlp-for-kannada)
+
+### Dataset Summary
+
+The Kannada news dataset contains only the headlines of news article in three categories:
+Entertainment, Tech, and Sports.
+
+The data set contains around 6300 news article headlines which are collected from Kannada news websites.
+The data set has been cleaned and contains train and test set using which can be used to benchmark topic classification models in Kannada.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+Kannada (kn)
+
+## Dataset Structure
+
+### Data Instances
+
+The data has two files. A train.csv and valid.csv. An example row of the dataset is as below:
+
+```
+{
+  'headline': 'ಫಿಫಾ ವಿಶ್ವಕಪ್ ಫೈನಲ್: ಅತಿರೇಕಕ್ಕೇರಿದ ಸಂಭ್ರಮಾಚರಣೆ; ಅಭಿಮಾನಿಗಳ ಹುಚ್ಚು ವರ್ತನೆಗೆ ವ್ಯಾಪಕ ಖಂಡನೆ',
+  'label':'sports'
+}
+```
+NOTE: The data has very few examples on the technology (class label: 'tech') topic. [More Information Needed]
+
+### Data Fields
+
+Data has two fields:
+- headline: text headline in kannada (string)
+- label : corresponding class label which the headlines pertains to in english (string)
+
+### Data Splits
+
+The dataset is divided into two splits. All the headlines are scraped from news websites on the internet.
+|                            | Tain   | Valid | 
+| -----                      | ------ | ----- |
+| Input Sentences            |  5167  | 1293  |
+
+## 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
+
+There are starkingly less amount of data for South Indian languages, especially Kannada, available in digital format which can be used for NLP purposes.
+Though having roughly 38 million native speakers, it is a little under-represented language and will benefit from active contribution from the community.
+
+This dataset, however, can just help people get exposed to Kannada and help proceed further active participation for enabling continuous progress and development.
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[Gaurav Arora] (https://github.com/goru001/nlp-for-kannada). Has also got some starter models an embeddings to help get started.
+
+### Licensing Information
+
+cc-by-sa-4.0
+
+### Citation Information
+
+[More Information Needed]

dataset_infos.json

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+{"default": {"description": "The Kannada news dataset contains only the headlines of news article in three categories:\nEntertainment, Tech, and Sports.\n\nThe data set contains around 6300 news article headlines which collected from Kannada news websites.\nThe data set has been cleaned and contains train and test set using which can be used to benchmark\nclassification models in Kannada.\n", "citation": "", "homepage": "", "license": "CC BY-SA 4.0", "features": {"headline": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["sports", "tech", "entertainment"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "kannada_news", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 969216, "num_examples": 5167, "dataset_name": "kannada_news"}, "validation": {"name": "validation", "num_bytes": 236817, "num_examples": 1293, "dataset_name": "kannada_news"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 1206033, "size_in_bytes": 1206033}}

dummy/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6948588924c81b0bf33249960bb06e8b7a1767cefc9ca72092cd006e95b35a9a
+size 1952

kannada_news.py

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+# coding=utf-8
+# 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.
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import datasets
+
+
+# no BibTeX citation
+_CITATION = ""
+
+_DESCRIPTION = """\
+The Kannada news dataset contains only the headlines of news article in three categories:
+Entertainment, Tech, and Sports.
+
+The data set contains around 6300 news article headlines which collected from Kannada news websites.
+The data set has been cleaned and contains train and test set using which can be used to benchmark
+classification models in Kannada.
+"""
+
+_LICENSE = "CC BY-SA 4.0"
+_TRAIN_FILENAME = "train.csv"
+_VALID_FILENAME = "valid.csv"
+
+
+class KannadaNews(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    @property
+    def manual_download_instructions(self):
+        return """\
+    \n    You need to go to https://www.kaggle.com/disisbig/kannada-news-dataset,
+    and manually download the dataset from Kaggle. Once it is completed,
+    a folder named archive.zip will appear in your Downloads folder(
+    or whichever folder your browser chooses to save files to). Unzip the folder to obtain
+    a folder named "archive" having train.csv and valid.csv.
+
+    You can then specify the path to this folder for the data_dir argument in the
+    datasets.load_dataset(...) option.
+
+    The <path/to/folder> can e.g. be "/Downloads/archive".
+    The data can then be loaded using the following command `datasets.load_dataset("kannada_news", data_dir="/Downloads/archive")`.
+    """
+
+    def _info(self):
+        class_names = ["sports", "tech", "entertainment"]
+        features = datasets.Features(
+            {
+                "headline": datasets.Value("string"),
+                "label": datasets.ClassLabel(names=class_names),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage="",
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        path_to_manual_file = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+
+        if not os.path.exists(path_to_manual_file):
+            raise FileNotFoundError(
+                "{} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('kannada_news', data_dir=...)` that includes a file name {}. Manual download instructions: {})".format(
+                    path_to_manual_file, _TRAIN_FILENAME, self.manual_download_instructions
+                )
+            )
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(path_to_manual_file, _TRAIN_FILENAME),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(path_to_manual_file, _VALID_FILENAME),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            rdr = csv.reader(f, delimiter=",")
+            next(rdr)
+            rownum = 0
+            for row in rdr:
+                rownum += 1
+                yield rownum, {
+                    "headline": row[0],
+                    "label": row[1],
+                }