A repo to Fine Tune BERT and use it for text classification.
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Updated
Jul 1, 2021 - Jupyter Notebook
A repo to Fine Tune BERT and use it for text classification.
Hate Speech classification in Italian using XLM (fine-tuning). Published at the WOAH workshop (NAACL2022).
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)
Final project for the "Deep Natural Language Processing" course @ PoliTo, 2022/2023
This repository hosts the code used in the paper Common vulnerability scoring system prediction based on open source intelligence information sources, which was originally a master's thesis by David Relke.
Deep Learning from basic to advance level include Bert for text intent classification
Application interface that use a pre-trained model based on BERT to extract information from aviation accidents texts. Laboratory Assignment II: Trainable Information Extraction. From AIW 2021.
FastAPI application for language detection
This project compares the performance of a Naive Bayes model and fine-tuned BERT models on emotion classification from text.
Completed as part of the "Natural Language Processing" course, this project employs the ArcEager parsing algorithm. Implementation is carried out using PyTorch and the Hugging Face library for utilizing pretrained BERT models.
Simple user-friendly webpage for spam classification of email and sms texts using a fine-tuned BERT base model (cased)
Prediksi Emosi App adalah sebuah aplikasi yang dapat digunakan untuk memprediksi 6 emosi yang muncul dalam sebuah kalimat, yaitu marah, sedih, senang, cinta, takut, dan jijik.
An Empirical Evaluation of Word Embedding Models for Subjectivity Analysis Tasks
Pre-training and fine tuning BERT models
Performing Text Extraction also known as Question-Answering using BERT,and serving it Via REST API.
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