This project aims to develop a model for analyzing the semantic similarity between pairs of tweets and providing a similarity score indicating the likelihood that they originated from the same user. Leveraging transformer-based architectures for text representation, the model utilizes advanced natural language processing (NLP) techniques to discern subtle semantic nuances present in tweet content.
The project is organized into the following components:
Data Preparation: Random sampling of tweet pairs and labeling them based on user similarity. Data Preprocessing: Cleaning and preprocessing tweet text for model input. Model Architecture: Utilization of a pre-trained transformer model for sequence classification.
Assessment of model performance using standard evaluation metrics.
- Python 3.x
- Pandas
- NumPy
- Transformers library (Hugging Face)
- TensorFlow
- scikit-learn
- nltk
- BeautifulSoup (for HTML tag removal)
Ensure the availability of tweet data in the required format (e.g., Excel files) and execute the create_tweet_pairs function to generate tweet pairs with appropriate labels.
Clean and preprocess tweet text using the provided functions for lowercase conversion, punctuation removal, and HTML tag removal.
Train the model using the provided script, specifying the desired transformer model and training parameters.
Evaluate the trained model using standard evaluation metrics such as accuracy, precision, recall, and F1 score. Model Deployment For deploying the trained model:
Save the trained model using appropriate serialization techniques (e.g., Trainer.save_model() or joblib.dump()). Consider deployment strategies such as REST API integration or model serving platforms for real-time inference.
Experiment with different transformer architectures and hyperparameters to optimize model performance. Explore techniques for model optimization and compression to reduce inference latency and resource consumption. Document any insights, challenges, and future directions in the project report.