File size: 2,851 Bytes
83d24b2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | from __future__ import annotations
from mteb.abstasks import AbsTaskClassification
from mteb.abstasks.TaskMetadata import TaskMetadata
class SanskritShlokasClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="SanskritShlokasClassification",
description="This data set contains ~500 Shlokas ",
reference="https://github.com/goru001/nlp-for-sanskrit",
dataset={
"path": "bpHigh/iNLTK_Sanskrit_Shlokas_Dataset",
"revision": "5a79d6472db143690c7ce6e974995d3610eee7f0",
},
type="Classification",
category="s2s",
date=("2019-01-01", "2020-01-01"),
eval_splits=["train", "validation"],
eval_langs=["san-Deva"],
main_score="accuracy",
form=["written"],
domains=["Religious"],
task_subtypes=["Topic classification"],
license="CC BY-SA 4.0",
socioeconomic_status="mixed",
annotations_creators="derived",
dialect=[],
text_creation="found",
bibtex_citation="""
@inproceedings{arora-2020-inltk,
title = "i{NLTK}: Natural Language Toolkit for Indic Languages",
author = "Arora, Gaurav",
editor = "Park, Eunjeong L. and
Hagiwara, Masato and
Milajevs, Dmitrijs and
Liu, Nelson F. and
Chauhan, Geeticka and
Tan, Liling",
booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlposs-1.10",
doi = "10.18653/v1/2020.nlposs-1.10",
pages = "66--71",
abstract = "We present iNLTK, an open-source NLP library consisting of pre-trained language models and out-of-the-box support for Data Augmentation, Textual Similarity, Sentence Embeddings, Word Embeddings, Tokenization and Text Generation in 13 Indic Languages. By using pre-trained models from iNLTK for text classification on publicly available datasets, we significantly outperform previously reported results. On these datasets, we also show that by using pre-trained models and data augmentation from iNLTK, we can achieve more than 95{\%} of the previous best performance by using less than 10{\%} of the training data. iNLTK is already being widely used by the community and has 40,000+ downloads, 600+ stars and 100+ forks on GitHub.",
}
""",
n_samples={"train": 383, "validation": 96},
avg_character_length={"train": 98.415, "validation": 96.635},
)
def dataset_transform(self):
self.dataset = self.dataset.rename_columns({"Sloka": "text", "Class": "label"})
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