Can we use explanations to improve hate speech models? Our paper accepted at AAAI 2021 tries to explore that question.
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Updated
Jun 12, 2023 - Python
Can we use explanations to improve hate speech models? Our paper accepted at AAAI 2021 tries to explore that question.
中文NER的那些事儿
Experiments for automated personality detection using Language Models and psycholinguistic features on various famous personality datasets including the Essays dataset (Big-Five)
This repository contains the official release of the model "BanglaBERT" and associated downstream finetuning code and datasets introduced in the paper titled "BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla" accpeted in Findings of the Annual Conference of the North American Chap…
Essential NLP & ML, short & fast pure Python code
A web extension for identifying dark pattern on websites powered by Fine Tuned BERT Model for classificaiton on dark pattern custom dataset,
A project based on Fine-tuned BERT to detect GLIBC vulnerabilities.
This repository contains code to reproduce the results in our paper "Transformers are Short Text Classifiers: A Study of Inductive Short Text Classifiers on Benchmarks and Real-world Datasets".
BERTMap: A BERT-Based Ontology Alignment System
Code for the FullStack AI Live Coding Series- Part 1 (CellStrat AI Lab)
BERT fine-tuning for POS tagging task (google's tensorflow)
Crisis Dataset for Benchmarks Experiments
Successfully developed a language detection transformer model that can accurately recognize the language in which any given text is written.
The evaluation of subjective answers has long been a challenge for educators, employers, and researchers. CheckMyAnswer, powered by machine learning algorithms, has emerged as a solution to this challenge.
This project consists of advanced phishing detection using the BERT masked language model.
In this project we have tried to do multi-label hate-speech classification in Bengali and Hindi language using fill-mask transformer models.
Unlock the power of Natural Language Processing (NLP) with our Automatic Document Summarization Model (ADSM), designed to effortlessly distill the essence of lengthy articles and research papers. Tired of drowning in information overload? Let our ADSM be your guide, providing crisp and coherent summaries, saving you valuable time and effort.
Class for Aspect-term extraction and Aspect-based sentiment analysis with BERT and Adapters
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