Applying Machine Learning/NLP Classification Techniques to Predict Relevant Emojis Given Textual Input

Trained a BERT (Bidirectional Encoder Representations from Transformers) model on 520K social media comments scraped from Reddit's API to develop a classification algorithm that predicts relevant emojis based on textual input, optimizing the model through adjustments of loss and optimization functions.

Packages and Technologies Used

• PyTorch: For training and deploying the BERT model for inference.
• Hugging Face Transformers: For using the BERT model.
• TensorFlow: For preprocessing text data using a Tokenizer (and training older models found in older_model_files).
• NumPy: For numerical operations and handling arrays in working with model output.
• pandas: For data manipulation, cleaning, and analysis of scraped data before training.
• Streamlit: Used Streamlit to host the app and provide a clean UI.

Files and Directories

• model_builder.py: Script that builds the model.
• individual_scrapes: Contains CSV files of various downloads of comments.
• emoji_prediction_model_torch_500: The main machine learning model directory, which includes a classification model predicting 500 of the most popular emojis in training data.
• /data_preprocessing/data_cleaner.py: Script for cleaning and optimizing input data for model training. This includes extracting and pairing emojis with text, and simplifying emojis by distilling optional emoji modifiers.
• /data_preprocessing/cleaned_data.csv: The main data file used for training the model.
• older_model_files: Contains various older models that perform worse than the current model. This includes an LSTM (Long Short-Term Memory) model and an unbalanced BERT model.
• app.py: The main Streamlit application script that runs the web interface for the emoji prediction model.
• requirements.txt: A file listing all the required Python packages for the project.

How to Use the Model

Model is hosted at [https://predict-emojis.streamlit.app/]