Duy Huynh
commited on
Commit
•
a973aa2
1
Parent(s):
47a7bd0
Initialize
Browse files- OV_Text.py +84 -0
- README.md +86 -1
- data/base/test.txt +0 -0
- data/base/train.txt +0 -0
- data/base/validation.txt +0 -0
- data/large/test.txt +0 -0
- data/large/train.txt +0 -0
- data/large/validation.txt +0 -0
- data/small/test.txt +0 -0
- data/small/train.txt +0 -0
- data/small/validation.txt +0 -0
- eval.py +16 -0
- statistics.py +26 -0
OV_Text.py
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import os
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import datasets
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_DESCRIPTION = """\
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OVText
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"""
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_CITATION = """\
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"""
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_BASE_URL = "https://huggingface.co/datasets/duyhngoc/OV_Text/resolve/main/data/"
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TRAIN_FILE = "train.txt"
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VALIDATION_FILE = "validation.txt"
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TEST_FILE = "test.txt"
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class UTSTextConfig(datasets.BuilderConfig):
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"""BuilderConfig"""
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def __init__(self, **kwargs):
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super(UTSTextConfig, self).__init__(**kwargs)
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class UTSText(datasets.GeneratorBasedBuilder):
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"""OV Word Tokenize datasets"""
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VERSION = datasets.Version("1.0.0")
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BUILDER_CONFIGS = [
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UTSTextConfig(
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name="small", version=VERSION, description="OV_Text Small"),
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UTSTextConfig(
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name="base", version=VERSION, description="OV_Text Base"),
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UTSTextConfig(
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name="large", version=VERSION, description="OV_Text Large")
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]
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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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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supervised_keys=None,
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homepage=None,
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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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subset_folder = self.config.name
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train_file = dl_manager.download(os.path.join(_BASE_URL, subset_folder, TRAIN_FILE))
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validation_file = dl_manager.download(os.path.join(_BASE_URL, subset_folder, VALIDATION_FILE))
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test_file = dl_manager.download(os.path.join(_BASE_URL, subset_folder, TEST_FILE))
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splits = [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={"filepath": train_file}
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={"filepath": validation_file}
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={"filepath": test_file}
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),
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]
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return splits
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def _generate_examples(self, filepath):
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print(filepath)
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guid = 0
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with open(filepath, encoding="utf-8") as f:
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for line in f:
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print(line)
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if line.strip() != "":
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item = {
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"text": line.strip()
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}
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yield guid, item
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guid += 1
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README.md
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---
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-
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---
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---
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annotations_creators:
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- no-annotation
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language:
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- vi
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license:
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- apache-2.0
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multilinguality:
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- monolingual
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pretty_name: OV_Text
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size_categories:
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- 10K<n<100K
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task_categories:
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- text-generation
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---
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# Dataset Card for OV_Text
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## Table of Contents
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- [Table of Contents](#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 and Leaderboards](#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-fields)
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- [Data Splits](#data-splits)
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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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- [Additional Information](#additional-information)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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The OV_Text dataset is a collection of 100,000 sentences sourced from various news articles.
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Out of the 10,000 sentences in the dataset, 5,000 sentences have a length ranging from 50 to 150, while the other 5,000 sentences have a length ranging from 20 to 50. This distribution of sentence lengths provides a diverse range of text samples that can be used to train and test natural language processing models.
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### Dataset Summary
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### Supported Tasks and Leaderboards
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### Languages
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## Dataset Structure
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### Data Instances
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### Data Fields
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### Data Splits
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| name | train | validation | test |
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|---------|--------:|-----------:|-------:|
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| small | 1600 | 200 | 200 |
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| base | 8000 | 1000 | 1000 |
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| large | 95000 | 2500 | 2500 |
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## Dataset Creation
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### Curation Rationale
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### Source Data
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### Annotations
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## Additional Information
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### Licensing Information
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The dataset is released under Apache 2.0.
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### Citation Information
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### Contributions
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data/base/test.txt
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data/base/train.txt
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data/base/validation.txt
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See raw diff
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data/large/test.txt
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The diff for this file is too large to render.
See raw diff
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data/large/train.txt
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See raw diff
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data/large/validation.txt
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The diff for this file is too large to render.
See raw diff
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data/small/test.txt
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See raw diff
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data/small/train.txt
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data/small/validation.txt
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eval.py
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from datasets import load_dataset
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import matplotlib.pyplot as plt
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from os.path import dirname, join
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dataset = load_dataset("duyhngoc/OV_Text")
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sentences = dataset["train"]["text"]
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# compute histogram of sentence lengths with bin size = 10
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pwd = dirname(__file__)
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tmp = join(pwd, "tmp")
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lengths = [len(s) for s in sentences]
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plt.hist(lengths, bins=range(0, max(lengths), 10))
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# add title of plot
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plt.title("Histogram of sentence lengths")
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plt.savefig(join(tmp, "histogram.png"))
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plt.show()
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statistics.py
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from os.path import dirname, join
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def count_lines(filepath):
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with open(filepath, encoding="utf-8") as f:
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lines = f.read().splitlines()
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return len(lines)
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def statistic(subset_folder):
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print("Train\t\t", count_lines(join(subset_folder, "train.txt")))
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print("Validation\t", count_lines(join(subset_folder, "validation.txt")))
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print("Test\t\t", count_lines(join(subset_folder, "test.txt")))
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pwd = dirname(__file__)
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dataset_folder = join(pwd, "data")
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print("Subset Small")
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statistic(join(dataset_folder, "small"))
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print("Subset Base")
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statistic(join(dataset_folder, "base"))
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print("Subset Large")
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statistic(join(dataset_folder, "large"))
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