Biomedical Question Answering system developed as part of my Masters' Thesis.
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Updated
May 13, 2021 - Python
Biomedical Question Answering system developed as part of my Masters' Thesis.
A binary text classifier that uses the ELECTRA model.
MSc thesis project: classification of italian certified electronic mails using SOTA machine learning, fine-tuning pre-trained deep learning models and data augmentation techniques
ML and Natual Language Processing
Official implementation of "Using Pre-Trained Language Models in an End-to-End Pipeline for Antithesis Detection" accepted in LREC-2024
Solving Math Word Problems Using Language Models and Contrastive Loss
Official implementation of "Using Pre-Trained Language Models in an End-to-End Pipeline for Antithesis Detection" accepted in LREC-2024
Simple NER Pipeline Using KoCharELECTRA
Work focus on Transformer model to Start classification (1-5) about reviews of YELP.
上海交通大学自然语言理解2020春:CoLA Task
Kaggle Aivle School 4th MiniProject 스팸메일 분류
Named entity recognition model with Dialog-ELECTRA
Korean Word-Spacing with KoCharELECTRA
Question Answering and Question Generation NLP tasks on the SQuAD v1.1 dataset
Baseline code for Korean open domain question answering(ODQA)
Code of paper: "ELECTRA is a Zero-shot learner, Too" -- Prompt-based ELECTRA for zero-shot learning.
Simple Text Classification[WIP]
Sarcasm is a term that refers to the use of words to mock, irritate, or amuse someone. It is commonly used on social media. The metaphorical and creative nature of sarcasm presents a significant difficulty for sentiment analysis systems based on affective computing. The technique and results of our team, UTNLP, in the SemEval-2022 shared task 6 …
Albert for Conversational Question Answering Challenge
AiSpace: Better practices for deep learning model development and deployment For Tensorflow 2.0
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