Search Results for author: Anindya Iqbal

Found 6 papers, 3 papers with code

BERT2Code: Can Pretrained Language Models be Leveraged for Code Search?

no code implementations16 Apr 2021 Abdullah Al Ishtiaq, Masum Hasan, Md. Mahim Anjum Haque, Kazi Sajeed Mehrab, Tanveer Muttaqueen, Tahmid Hasan, Anindya Iqbal, Rifat Shahriyar

In this work, we leverage the efficacy of these embedding models using a simple, lightweight 2-layer neural network in the task of semantic code search.

Code Search

BanglaBERT: Combating Embedding Barrier in Multilingual Models for Low-Resource Language Understanding

1 code implementation1 Jan 2021 Abhik Bhattacharjee, Tahmid Hasan, Kazi Samin, Md Saiful Islam, M. Sohel Rahman, Anindya Iqbal, Rifat Shahriyar

As a bi-product of the standard NLU benchmarks, we introduce a new downstream dataset on natural language inference (NLI) and show that BanglaBERT outperforms previous state-of-the-art results on all tasks by up to 3. 5%.

Document Classification Language Modelling +3

Static and Animated 3D Scene Generation from Free-form Text Descriptions

no code implementations4 Oct 2020 Faria Huq, Nafees Ahmed, Anindya Iqbal

As the choice of words and syntax vary while preparing a textual description, it is challenging for the system to reliably produce a consistently desirable output from different forms of language input.

Language Modelling Scene Generation

Early Prediction for Merged vs Abandoned Code Changes in Modern Code Reviews

no code implementations7 Dec 2019 Md. Khairul Islam, Toufique Ahmed, Rifat Shahriyar, Anindya Iqbal, Gias Uddin

In our empirical study on the 146, 612 code changes from the three software projects, we find that (1) The new features like reviewer dimensions that are introduced in PredCR are the most informative.

A Benchmark Study of Machine Learning Models for Online Fake News Detection

1 code implementation12 May 2019 Junaed Younus Khan, Md. Tawkat Islam Khondaker, Sadia Afroz, Gias Uddin, Anindya Iqbal

In this research, we conducted a benchmark study to assess the performance of different applicable machine learning approaches on three different datasets where we accumulated the largest and most diversified one.

Fake News Detection

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