You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
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 and a tranformer model. Also we collected a dataset for NER task.
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.
Extracting appropriate named entities using both NLTK and SpaCy and identifying the most frequently mentioned company in the given news articles and tweets.
To explore the LLMs such as GPT-3. Demonstrate the strategies to design prompts are reproducible and produce a consistent result. Set up an MLOps pipeline that helps automate the task of using different LLMs and different topics. Allow improvements in the prompt design to be integrated without breaking the system and centralized log system shoul…