5 citations · 12 across the 7 of their papers we have counts for
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L3Cube-MahaSTS: A Marathi Sentence Similarity Dataset and Models
Aishwarya Mirashi, Ananya Joshi, Raviraj Joshi
We present MahaSTS, a human-annotated Sentence Textual Similarity (STS) dataset for Marathi, along with MahaSBERT-STS-v2, a fine-tuned Sentence-BERT model optimized for regression-…
MahaParaphrase: A Marathi Paraphrase Detection Corpus and BERT-based Models
Suramya Jadhav, Abhay Shanbhag, Amogh Thakurdesai +3
Paraphrases are a vital tool to assist language understanding tasks such as question answering, style transfer, semantic parsing, and data augmentation tasks. Indic languages are c…
Universal Cross-Lingual Text Classification
Riya Savant, Anushka Shelke, Sakshi Todmal +3
Text classification, an integral task in natural language processing, involves the automatic categorization of text into predefined classes. Creating supervised labeled datasets fo…
Harnessing Pre-Trained Sentence Transformers for Offensive Language Detection in Indian Languages
Ananya Joshi, Raviraj Joshi
In our increasingly interconnected digital world, social media platforms have emerged as powerful channels for the dissemination of hate speech and offensive content. This work del…
L3Cube-IndicSBERT: A simple approach for learning cross-lingual sentence representations using multilingual BERT
Samruddhi Deode, Janhavi Gadre, Aditi Kajale +2
The multilingual Sentence-BERT (SBERT) models map different languages to common representation space and are useful for cross-language similarity and mining tasks. We propose a sim…
L3Cube-MahaSBERT and HindSBERT: Sentence BERT Models and Benchmarking BERT Sentence Representations for Hindi and Marathi
Ananya Joshi, Aditi Kajale, Janhavi Gadre +2
Sentence representation from vanilla BERT models does not work well on sentence similarity tasks. Sentence-BERT models specifically trained on STS or NLI datasets are shown to prov…