This repository contains my scripts, results and visualization for my bachelor thesis "Medical concept PROBLEM: Polarity, Modality and Temporal Relations" - ON GOING, doing some code reorganize
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Updated
Nov 27, 2023 - Python
This repository contains my scripts, results and visualization for my bachelor thesis "Medical concept PROBLEM: Polarity, Modality and Temporal Relations" - ON GOING, doing some code reorganize
This is the official implementation of our paper Robust Hate Speech Detection via Mitigating Spurious Correlations (Tiwari et al., AACL-IJCNLP 2022)
Implementation of MLops pipeline for Named Entity Recognition (NER) using pretrained Huggingface BERT transformer based model, further use CircleCI CI/CD tool for deployment on google cloud platform by using Docker image, Flask front end interface.
This project benchmarks various BERT-based models on the IMDB movie review dataset for sentiment classification, evaluating accuracy, precision, recall, and F1 score.
Performing Text Extraction also known as Question-Answering using BERT,and serving it Via REST API.
API for performing named entity recognition from text input in Finnish.
Code for identify the topics responsible for the propagation of hate speech on the social network platform. Project for 'NLP' at University of Twent
Code for training Finnish named entity recognition (NER) model based on BERT.
BERT Model 1: Annotating and Fine-Tuning Deep-Learning Model for Binary Text Classification
Implementation of LoRA from scratch for fine tuning
Fine-tuned BERT model for multi-class uncertainty cues recognition.
Text generation using a fine tuned BERT on the onion news dataset
NLP to classify news articles into catagories
DACON 기후기술분류 경진대회 at @KNU-BrainAI (2021)
Its main goal wasn't to develop the solution with the best accuracy among others developed by Kaggle's community but rather to understand the applicability of the MLflow framework and how to use it to track different models during the training and evaluation steps, while also learning about the model's versioning and registry.
This cryptocurrency market sentiment analysis tool leverages a fine-tuned RoBERTa model for 98% accurate sentiment classification of cryptocurrency-related news articles. It uses Selenium and BeautifulSoup for web scraping news articles and provides insightful market sentiment analysis and data visualization
Scrap, token classification and model deployment for a selective process.
A set of Python scripts that creates a dataset of bill texts and subjects, then fine-tunes BERT on the dataset.
Applying Natural Language Processing Techniques to classify text from various sources
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