This repository contains the code and resources for final research project that combines BART and BERT models using the ensemble method. The main goal is to improve Aspect-based Sentiment Analysis (ABSA) model performance for the Indonesia’s Tourism Destinations dataset (Study case: Borobudur and Prambanan temples).
- Model Components: We utilize a pretrained model BARTLARGE model for BART and a BERTBASE Uncased model for BERT. These models are fine-tuned on the dataset for enhanced the performance.
- Ensemble Library: The ensemble transformers library is employed to seamlessly combine the two models.
- Dataset: The dataset used for fine-tuning can be found at this GitHub repository.
- Fine-Tuning:
- BART: Fine-tuned using the the dataset
- BERT: Similarly, fine-tuned on the dataset
- Layer Modification:
- For each fine-tuned model (BART and BERT), the final layer (usually known as head) is modified by adding a linear layer.
- The ensemble model predicts by averaging the class probabilities from both models.
Take a peek at what the Streamlit app interface looks like:
Check out the video demo of the model below:
- BART: Download BARTLARGE model
- BERT: Download BERTBASE Uncased model
- Dataset: Gold Aspect-Based Dataset for Borobudur and Prambanan
🧞♂️ TASI-2324-118
1. 12S20024 - Sandro Sinaga
2. 12S20042 - Mastawila F. Simanjuntak
3. 12S20048 - Jevania
Supervisor I : Samuel I. G. Situmeang, S.TI., M.Sc.
Supervisor II: Sarah R. Tambunan, S.Kom, M.Sc.
© Information System Study Program, Faculty of Informatics and Electrical Engineering, Institut Teknologi Del, 2024
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