2 citations · 2 across the 7 of their papers we have counts for
4 papers · 1 filter
Comparative Study of Pre-Trained BERT and Large Language Models for Code-Mixed Named Entity Recognition
Mayur Shirke, Amey Shembade, Pavan Thorat +2
Named Entity Recognition (NER) in code-mixed text, particularly Hindi-English (Hinglish), presents unique challenges due to informal structure, transliteration, and frequent langua…
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-…
On Importance of Layer Pruning for Smaller BERT Models and Low Resource Languages
Mayur Shirke, Amey Shembade, Madhushri Wagh +2
This study explores the effectiveness of layer pruning for developing more efficient BERT models tailored to specific downstream tasks in low-resource languages. Our primary object…
Towards Building Efficient Sentence BERT Models using Layer Pruning
Anushka Shelke, Riya Savant, Raviraj Joshi
This study examines the effectiveness of layer pruning in creating efficient Sentence BERT (SBERT) models. Our goal is to create smaller sentence embedding models that reduce compl…