WIP code and open materials: 'Passive suicidality in a repressive U.S. political context.' Additional materials hosted on Open Science Framework.
-
Updated
Oct 8, 2024 - Jupyter Notebook
WIP code and open materials: 'Passive suicidality in a repressive U.S. political context.' Additional materials hosted on Open Science Framework.
Public release of SciFCheX system developed for COM3610 Dissertation Project at the University of Sheffield. The pipeline is designed to perform fact-checking on scientific claims.
Code and text for my master's thesis
This repository powers a Streamlit app for classifying text with respect to 16 of United Nations Sustainable Development Goals (SDG)..
NLP tutorial on fine-tuning the BERT model to classify IMDb reviews: mixed, positive, negative.
Digit Publication Platform
This repository aims to outline an architecture using GPT 4, BERT, DistilBERT, and other models to target an automated architecture for creating artificial data for chatbots, validating these data, and data augmentation, based on published articles on the topics.
Understanding and Fine-Tuning BERT for NLP Tasks
Model Deep Learning berbasis IndoBERT yang dapat memprediksi 6 jenis emosi dalam suatu kalimat, yaitu marah, sedih, senang, cinta, takut, dan jijik.
Code for the paper: Transformers and large language models are efficient feature extractors for electronic health record studies
In this we explore into a Question Answering task on structured relational data (Tables) and CSV data
Persian text emotion recognition by fine tuning the XLM-RoBERTa Model + Bidirectional GRU layer.
Web application designed to help users determine if a given URL is malicious or not. Run by Finetuned BERT for classification task
This project benchmarks various BERT-based models on the IMDB movie review dataset for sentiment classification, evaluating accuracy, precision, recall, and F1 score.
A naive sentiment classifier for IMDb
Linear Predictive Coding based Tokenizer for self-supervised learning of time series via BERT. Article: https://arxiv.org/abs/2408.07292
Document Level Sentiment Analysis is an End-to-End deep learning workflow using Hugging Face transformers API to do a "classification" task at document level, to analyze the sentiment of input document containing English sentences or paragraphs.
This project uses a fine-tuned BERT model for tweet classification, focusing on detecting bullying in tweets. The model leverages BERT's advanced language understanding to effectively identify and categorize harmful or abusive language.
Add a description, image, and links to the bert-fine-tuning topic page so that developers can more easily learn about it.
To associate your repository with the bert-fine-tuning topic, visit your repo's landing page and select "manage topics."