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Parent(s):
Update files from the datasets library (from 1.2.0)
Browse filesRelease notes: https://github.com/huggingface/datasets/releases/tag/1.2.0
- .gitattributes +27 -0
- README.md +140 -0
- dummy/1.1.0/dummy_data.zip +3 -0
- telugu_books.py +104 -0
.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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annotations_creators:
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- expert-generated
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language_creators:
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- expert-generated
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languages:
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- te
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licenses:
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- unknown
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multilinguality:
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- monolingual
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size_categories:
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- n<1K
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source_datasets:
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- original
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task_categories:
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- sequence-modeling
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task_ids:
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- language-modeling
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---
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# Dataset Card for [telugu_books]
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## Table of Contents
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-instances)
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- [Data Splits](#data-instances)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Annotations](#annotations)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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## Dataset Description
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- **Homepage:**
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[Telugu Books](https://www.kaggle.com/sudalairajkumar/telugu-nlp)
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- **Repository:**
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- **Paper:**
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- **Leaderboard:**
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- **Point of Contact:**
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### Dataset Summary
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This dataset is created by scraping telugu novels from teluguone.com this dataset can be used for nlp tasks like topic modeling, word embeddings, transfer learning etc
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### Supported Tasks and Leaderboards
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[More Information Needed]
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### Languages
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TE - Telugu
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## Dataset Structure
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### Data Instances
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[More Information Needed]
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### Data Fields
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- Text: Sentence from a novel
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### Data Splits
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[More Information Needed]
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## Dataset Creation
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### Curation Rationale
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[More Information Needed]
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### Source Data
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#### Initial Data Collection and Normalization
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[More Information Needed]
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#### Who are the source language producers?
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Anusha Motamarri
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### Annotations
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#### Annotation process
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Anusha Motamarri
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#### Who are the annotators?
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Anusha Motamarri
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### Personal and Sensitive Information
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[More Information Needed]
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## Considerations for Using the Data
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### Social Impact of Dataset
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[More Information Needed]
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### Discussion of Biases
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[More Information Needed]
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### Other Known Limitations
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[More Information Needed]
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## Additional Information
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### Dataset Curators
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[More Information Needed]
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### Licensing Information
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[More Information Needed]
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### Citation Information
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[More Information Needed]
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dummy/1.1.0/dummy_data.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:712ecb81cfb84e571e2a6608bb14a9b3978cf0ee531efe17e7162e264ba6a0ba
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size 2320
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telugu_books.py
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# coding=utf-8
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Telugu Books Dataset"""
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from __future__ import absolute_import, division, print_function
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import csv
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import os
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import datasets
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_CITATION = """\
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@InProceedings{huggingface:dataset,
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title = {Indic NLP - Natural Language Processing for Indian Languages},
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authors = {Sudalai Rajkumar, Anusha Motamarri},
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year={2019}
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}
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"""
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_DESCRIPTION = """\
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This dataset is created by scraping telugu novels from teluguone.com this dataset can be used for nlp tasks like topic modeling, word embeddings, transfer learning etc
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"""
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_HOMEPAGE = "https://www.kaggle.com/sudalairajkumar/telugu-nlp"
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_LICENSE = "Data files © Original Authors"
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_FILENAME = "telugu_books.csv"
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class TeluguBooks(datasets.GeneratorBasedBuilder):
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"""Telugu novels"""
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VERSION = datasets.Version("1.1.0")
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@property
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def manual_download_instructions(self):
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return """\
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You need to go to https://www.kaggle.com/sudalairajkumar/telugu-nlp,
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and manually download the telugu_books. Once it is completed,
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a file named telugu_books.zip will be appeared in your Downloads folder
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or whichever folder your browser chooses to save files to. You then have
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to unzip the file and move telugu_books,csv under <path/to/folder>.
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The <path/to/folder> can e.g. be "~/manual_data".
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telugu_books can then be loaded using the following command `datasets.load_dataset("telugu_books", data_dir="<path/to/folder>")`.
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"""
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def _info(self):
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features = datasets.Features(
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{
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"text": datasets.Value("string"),
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}
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)
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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supervised_keys=None,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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path_to_manual_file = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
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if not os.path.exists(path_to_manual_file):
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raise FileNotFoundError(
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"{} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('telugu_books', data_dir=...)` that includes file name {}. Manual download instructions: {}".format(
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path_to_manual_file,
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_FILENAME,
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self.manual_download_instructions,
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)
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)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": os.path.join(path_to_manual_file, "telugu_books.csv"),
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"split": "train",
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},
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),
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]
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def _generate_examples(self, filepath, split):
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""" Yields examples. """
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with open(filepath, encoding="utf-8") as csv_file:
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csv_reader = csv.reader(csv_file)
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for id_, row in enumerate(csv_reader):
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_, text = row
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yield id_, {"text": text}
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