sentiment-analysis-model

Sentiment Analysis Model (SAM)

A sentiment analysis model built using the Burn deep learning framework in Rust, fine-tuned on the MTEB Tweet Sentiment Extraction dataset and exposed via a Rocket API.

🧠 Model Details

• Architecture: Transformer Encoder with 6 layers, 4 attention heads, d_model=256, and d_ff=1024.
• Embeddings: Token and positional embeddings with a maximum sequence length of 256.
• Output Layer: Linear layer mapping to 3 sentiment classes: Negative, Neutral, Positive.
• Activation Function: Softmax for multi-class classification.
• Dropout: Applied with a rate of 0.1 to prevent overfitting (one for embeddings and one for the output layer).
• Training Framework: Burn in Rust.

📚 Training Data

• Dataset: MTEB Tweet Sentiment Extraction
• Size: 100,000 training samples.
• Preprocessing: Utilized the BertCasedTokenizer for tokenization.
• Batching: Mini-batch gradient descent with a batch size of 32.

⚙️ Training Configuration

• Optimizer: AdamW with weight decay (0.01) and learning rate (1e-4) - especially good for training large models.
• Learning Rate Scheduler: Noam scheduler with 5,000 warm-up steps - especially useful for transformer models.
• Loss Function: CrossEntropyLoss with label smoothing (0.1) and class balancing.
• Gradient Clipping: Applied with a maximum norm of 1.0.
• Early Stopping: Implemented with a patience of 2 epochs.
• Epochs: Trained for up to 5 epochs with early stopping based on validation loss.

📈 Evaluation Metrics

• Learner Summary:

TextClassificationModel {
  transformer: TransformerEncoder {d_model: 256, d_ff: 1024, n_heads: 8, n_layers: 4, dropout: 0.1, norm_first: true, quiet_softmax: true, params: 3159040}
  embedding_token: Embedding {n_embedding: 28996, d_model: 256, params: 7422976}
  embedding_pos: Embedding {n_embedding: 256, d_model: 256, params: 65536}
  embed_dropout: Dropout {prob: 0.1}
  output_dropout: Dropout {prob: 0.1}
  output: Linear {d_input: 256, d_output: 3, bias: true, params: 771}
  n_classes: 3
  max_seq_length: 256
  params: 10648323
}

• TODO:
  • Tweak hyperparameters to alleviate underfitting.
  • Enhance logging and monitoring.

🚀 Usage

• API Endpoint: /predict
• Example Request:

{
  "text": "I love the new features in this app!"
}

• Example Response:

{
  "sentiment": "Positive"
}

• Steps to Run: TODO after dockerizing and deploying to Hugging Face Spaces.