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:
+- machine-generated
+language_creators:
+- other
+languages:
+- te
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- sequence-modeling
+- text-classification
+task_ids:
+- language-modeling
+- multi-class-classification
+- topic-classification
+---
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Dataset Card for [Dataset Name]](#dataset-card-for-dataset-name)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  - [Dataset Creation](#dataset-creation)
+    - [Curation Rationale](#curation-rationale)
+    - [Source Data](#source-data)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+      - [Who are the source language producers?](#who-are-the-source-language-producers)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+    - [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: https://www.kaggle.com/sudalairajkumar/telugu-nlp?select=telugu_news
+- **Repository: https://github.com/AnushaMotamarri/Telugu-Newspaper-Article-Dataset
+
+
+### Dataset Summary
+
+This dataset contains Telugu language news articles along with respective topic 
+labels (business, editorial, entertainment, nation, sport) extracted from the daily Andhra Jyoti. 
+This dataset could be used to build Classification and Language Models.
+
+### Supported Tasks and Leaderboards
+
+Multiclass classification, Topic Classification, Language Model
+
+### Languages
+
+TE - Telugu, India
+
+## Dataset Structure
+
+
+### Data Instances
+
+Two CSV files (train, test) with five columns (sno, date, heading, body, topic).
+
+### Data Fields
+
+- sno: id
+- date: publish date of the news article
+- heading: article heading/title
+- body: article body/content
+- topic: one of the following topics (business, editorial, entertainment, nation, sport)
+
+### Data Splits
+
+Train and Test
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+- https://www.kaggle.com/sudalairajkumar/telugu-nlp?select=telugu_news
+- https://github.com/AnushaMotamarri/Telugu-Newspaper-Article-Dataset
+
+#### Initial Data Collection and Normalization
+
+The source data is scraped articles from archives of Telugu newspaper website Andhra Jyoti. 
+A set of queries were created and the corresponding ground truth answers were retrieved by a combination of BM25 and tf-idf.
+
+#### 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
+
+Sudalai Rajkumar, Anusha Motamarri
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]

dataset_infos.json

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+{"default": {"description": "This dataset contains Telugu language news articles along with respective\ntopic labels (business, editorial, entertainment, nation, sport) extracted from\nthe daily Andhra Jyoti. This dataset could be used to build Classification and Language Models.\n", "citation": "@InProceedings{kaggle:dataset,\ntitle = {Telugu News - Natural Language Processing for Indian Languages},\nauthors={Sudalai Rajkumar, Anusha Motamarri},\nyear={2019}\n}\n", "homepage": "https://www.kaggle.com/sudalairajkumar/telugu-nlp", "license": "Data files \u00a9 Original Authors", "features": {"sno": {"dtype": "int32", "id": null, "_type": "Value"}, "date": {"dtype": "string", "id": null, "_type": "Value"}, "heading": {"dtype": "string", "id": null, "_type": "Value"}, "body": {"dtype": "string", "id": null, "_type": "Value"}, "topic": {"num_classes": 5, "names": ["business", "editorial", "entertainment", "nation", "sports"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "telugu_news", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 69400234, "num_examples": 17312, "dataset_name": "telugu_news"}, "test": {"name": "test", "num_bytes": 17265514, "num_examples": 4329, "dataset_name": "telugu_news"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 86665748, "size_in_bytes": 86665748}}

dummy/1.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1e1b332352c7cc6c4c5f185de198510825889009aecb5e87e6daf3c5dc8fffe7
+size 5754

telugu_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.
+"""TODO: Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import datasets
+
+
+_CITATION = """\
+@InProceedings{kaggle:dataset,
+title = {Telugu News - Natural Language Processing for Indian Languages},
+authors={Sudalai Rajkumar, Anusha Motamarri},
+year={2019}
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+This dataset contains Telugu language news articles along with respective
+topic labels (business, editorial, entertainment, nation, sport) extracted from
+the daily Andhra Jyoti. This dataset could be used to build Classification and Language Models.
+"""
+
+_HOMEPAGE = "https://www.kaggle.com/sudalairajkumar/telugu-nlp"
+
+_LICENSE = "Data files © Original Authors"
+
+
+class TeluguNews(datasets.GeneratorBasedBuilder):
+    """Telugu News Articles with Topics."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    @property
+    def manual_download_instructions(self):
+        return """\
+    You need to visit Kaggle @ https://www.kaggle.com/sudalairajkumar/telugu-nlp,
+    and manually download the `telugu_news` dataset. This will download a file called
+    `telugu_news.zip` to your laptop. Unzip the file and move the two CSV files
+    (train and test) files to <path/to/folder>. You can then use
+    `datasets.load_dataset("telugu_news", data_dir="<path/to/folder>")` to load the datset.
+    """
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "sno": datasets.Value("int32"),
+                "date": datasets.Value("string"),
+                "heading": datasets.Value("string"),
+                "body": datasets.Value("string"),
+                "topic": datasets.features.ClassLabel(
+                    names=["business", "editorial", "entertainment", "nation", "sports"],
+                ),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+
+        if not os.path.exists(data_dir):
+            raise FileNotFoundError(
+                "{} does not exist. Download instructions: {} ".format(
+                    data_dir,
+                    self.manual_download_instructions,
+                )
+            )
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train_telugu_news.csv"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "test_telugu_news.csv"),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples. """
+
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(csv_file)
+            next(csv_reader, None)  # skip the headers
+
+            for id_, row in enumerate(csv_reader):
+                sno, date, heading, body, topic = row
+                yield id_, {
+                    "sno": sno,
+                    "date": date,
+                    "heading": heading,
+                    "body": body,
+                    "topic": topic,
+                }