This project implements a lightweight fine-tuning approach using Low-Rank Adaptation (LoRA) on the GPT-2 model for sentiment analysis. The Stanford Sentiment Treebank (SST-2) dataset is used for fine-tuning and evaluation.
Key Components
PEFT Technique: Low-Rank Adaptation (LoRA)
Base Model: GPT-2 (gpt2)
Task: Sentiment Analysis
Dataset: Stanford Sentiment Treebank (SST-2)
Evaluation Approach: Accuracy metric with Hugging Face's Trainer
How to Get Started with the Model
The implementation is available in the Jupyter notebook LightweightFineTuning.ipynb. To run the notebook:
Ensure you have the required dependencies installed:
Open and run the notebook to see the implementation details, training process, and evaluation results.
Training Details
Training Data
The model is fine-tuned on the Stanford Sentiment Treebank (SST-2) dataset, which contains movie reviews labeled with binary sentiment (positive/negative).
Training Procedure
The training uses LoRA, which adds trainable rank decomposition matrices to existing weights while freezing the original model parameters. This significantly reduces the number of trainable parameters compared to full fine-tuning.
Training Hyperparameters
PEFT Method: LoRA
Rank: 8
Alpha: 16
Dropout: 0.1
Training Regime: Mixed precision (fp16)
Evaluation
Metrics
The model is evaluated using accuracy on the SST-2 validation set.
Results
The fine-tuned model demonstrates improved performance on sentiment analysis compared to the base model, while requiring significantly fewer trainable parameters.
Technical Specifications
Compute Infrastructure
The model was trained using PyTorch and the Hugging Face Transformers and PEFT libraries.
Framework Versions
PEFT 0.14.0
Transformers (Hugging Face)
PyTorch
Datasets (Hugging Face)
Model Card for Model ID
Model Details
Model Description
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Uses
Direct Use
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
