bge-base-en-v1.5-course-recommender-v5
| Property | Value |
|---|---|
| Model Type | Sentence Transformer |
| Base Model | BAAI/bge-base-en-v1.5 |
| Output Dimensions | 768 |
| Max Sequence Length | 512 tokens |
| Training Samples | 45 |
| Framework | PyTorch 2.2.2 |
What is bge-base-en-v1.5-course-recommender-v5?
This is a specialized sentence transformer model fine-tuned for course recommendation tasks. Built upon the BAAI/bge-base-en-v1.5 architecture, it's designed to convert educational content and course descriptions into 768-dimensional dense vector representations that capture semantic meaning, enabling advanced course recommendation and similarity matching.
Implementation Details
The model implements a three-component architecture consisting of a transformer encoder, pooling layer, and normalization layer. It was trained using MultipleNegativesRankingLoss with a scale factor of 20.0 and cosine similarity as the comparison metric. The training process involved 45 carefully curated course description pairs, with evaluation performed on 5 test samples.
- Custom pooling configuration with CLS token emphasis
- Normalized output embeddings for consistent similarity calculations
- Fine-tuned with a learning rate of 3e-06 and 24 maximum steps
- Batch size of 16 with warmup ratio of 0.1
Core Capabilities
- Semantic textual similarity for course descriptions
- Course recommendation based on content similarity
- Semantic search across educational content
- Course clustering and classification
- Paraphrase detection in educational materials
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for educational content analysis, with custom training on course descriptions that enables precise semantic matching and recommendation in educational contexts. Its architecture is designed to capture nuanced relationships between course materials while maintaining computational efficiency.
Q: What are the recommended use cases?
The model excels at tasks such as finding similar courses, building course recommendation systems, organizing educational content by topic, and semantic search across course catalogs. It's particularly effective for educational platforms and learning management systems seeking to implement advanced content discovery features.
