An AI-powered, but model-agnostic name-entity recognition toolkit.
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Updated
Jul 12, 2024 - Python
An AI-powered, but model-agnostic name-entity recognition toolkit.
It's the third homework of Natural Language Processing course in Spring 2024 at Sharif University of Technology. It's about sentiment analysis in the level of texts. And also in the level of words, we do NER (Name Entity Recognition). In each part we designed a base model (for example SVM or LSTM) and a tranformer model. Also we collected a dataset
This project was developed for a Kaggle competition focused on detecting Personally Identifiable Information (PII) in student writing. The primary objective was to build a robust model capable of identifying PII with high recall. The DeBERTa v3 transformer model was chosen for this task after comparing its performance with other transformer models.
Hugging Face Transformers, a popular Python library, offers pre-trained models for various powerful toolkit for NLP tasks, opening doors to career opportunities and be part of the innovation that will change the world with shaping the future of human-machine interaction.
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