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"""Sanskrit OCR Post-Correction Dataset"""
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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{maheshwari2022benchmark,
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title={A Benchmark and Dataset for Post-OCR text correction in Sanskrit},
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author={Maheshwari, Ayush and Singh, Nikhil and Krishna, Amrith and Ramakrishnan, Ganesh},
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booktitle={Findings of the Association for Computational Linguistics: EMNLP 2022},
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pages={6258--6265},
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year={2022}
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}
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"""
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_DESCRIPTION = """\
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A Benchmark and Dataset for Post-OCR text correction in Sanskrit.
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This dataset contains manually post-edited OCR data for Sanskrit texts in Devanagari script.
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It includes:
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- Train/Validation/Test splits with OCR text and corrected ground truth
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- An out-of-domain test set of 500 sentences
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- Source texts from classical Sanskrit works including Brahmasutra Bhashyam, Grahalaghava, and Goladhyaya
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"""
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_HOMEPAGE = "https://github.com/ayushbits/pe-ocr-sanskrit"
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_LICENSE = "MIT"
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_URLS = {
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"train": "train_devnagari.csv",
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"validation": "val_devnagari.csv",
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"test": "test_devnagari.csv",
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"ood_test": "ood-test.csv",
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}
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class SanskritOCRPostCorrection(datasets.GeneratorBasedBuilder):
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"""Sanskrit OCR Post-Correction Dataset"""
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VERSION = datasets.Version("1.0.0")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(
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name="default",
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version=VERSION,
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description="Sanskrit OCR post-correction dataset with all splits",
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),
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]
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DEFAULT_CONFIG_NAME = "default"
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def _info(self):
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features = datasets.Features(
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{
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"input_text": datasets.Value("string"),
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"target_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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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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data_files = dl_manager.download_and_extract(_URLS)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"filepath": data_files["train"],
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"split": "train",
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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"filepath": data_files["validation"],
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"split": "validation",
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"filepath": data_files["test"],
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"split": "test",
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},
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),
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datasets.SplitGenerator(
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name="ood_test",
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gen_kwargs={
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"filepath": data_files["ood_test"],
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"split": "ood_test",
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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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delimiter = ";" if split == "ood_test" else ","
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with open(filepath, encoding="utf-8") as f:
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reader = csv.DictReader(f, delimiter=delimiter)
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for idx, row in enumerate(reader):
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yield idx, {
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"input_text": row["input_text"],
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"target_text": row["target_text"],
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}
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