sklearn-transformers
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Framework | Scikit-learn + Transformers |
| Base Model | facebook/bart-base |
What is sklearn-transformers?
sklearn-transformers is an innovative pipeline that bridges the gap between Hugging Face transformers and traditional machine learning by combining BART embeddings with scikit-learn's Logistic Regression classifier. This proof-of-concept implementation demonstrates how modern transformer architectures can be integrated with classical ML algorithms for effective sentiment analysis.
Implementation Details
The model implements a two-step pipeline: first, it uses the facebook/bart-base transformer model to generate text embeddings through the HFTransformersLanguage component from whatlies library. These embeddings are then fed into a Logistic Regression classifier with L2 regularization, achieving impressive classification metrics with 87% accuracy across both positive and negative sentiments.
- Precision and recall scores of 0.85-0.89 across classes
- Balanced F1-score of 0.87
- Utilizes LBFGS solver with L2 penalty
Core Capabilities
- Sentiment analysis with binary classification
- Text embedding generation using BART
- Scalable processing with scikit-learn integration
- Interactive pipeline visualization
Frequently Asked Questions
Q: What makes this model unique?
This model uniquely combines the power of modern transformer-based embeddings with the simplicity and interpretability of traditional machine learning classifiers, making it both powerful and practical for production environments.
Q: What are the recommended use cases?
The model is particularly well-suited for sentiment analysis tasks, especially in scenarios where interpretability and efficiency are prioritized alongside performance. It's ideal for production environments that require a balance between sophisticated language understanding and computational efficiency.
