no code implementations • 20 Mar 2024 • Mitodru Niyogi, Arnab Bhattacharya
We also evaluated the responses of prompts for instruction-tuned models by GPT-3. 5-Turbo on clarity, relevance, completeness, and legal reasoning metrics in a scale of 10.
no code implementations • 8 Feb 2024 • Mitodru Niyogi
In this master thesis, we present sequence-to-sequence deep learning models and training paradigms to map NL to general-purpose programming languages that can assist users with suggestions of source code snippets, given a NL intent, and also extend auto-completion functionality of the source code to users while they are writing source code.
no code implementations • 31 Jan 2024 • Mitodru Niyogi, Arnab Bhattacharya
It is a collection of auto-regressive monolingual, bilingual, and multilingual Indic language models pretrained from scratch on a single GPU for 10 Indian languages (Assamese, Bangla, Hindi, Konkani, Maithili, Marathi, Odia, Sanskrit, Tamil, Telugu) across 5 scripts (Bangla, Devanagari, Odia, Tamil, Telugu) of varying sizes ranging from 13. 29M to 367. 5M. The models are pretrained with a context size of 1024 on a single GPU.
no code implementations • 12 Apr 2018 • Mitodru Niyogi, Kripabandhu Ghosh, Arnab Bhattacharya
The proposed method is superior to the state-of-the-art method not only for IR evaluation measures but also in terms of time requirements.
no code implementations • 11 Nov 2017 • Mitodru Niyogi, Asim K. Pal
As one showcase, in this paper, we summarize the data set of Twitter messages related to recent demonetization of all Rs.