# TODO - Embedding Trainer
## High Priority 🔴
### 1. Fix Training Algorithm
- [ ] **Debug gradient calculation** - Current implementation produces zero embeddings
- Investigate why embeddings are not being updated during training
- Verify gradient calculation and weight update logic
- Test with simple synthetic data to isolate the issue
- Implement proper loss function and backpropagation
- [ ] **Implement negative sampling** - Improve training efficiency
- Add negative sampling for Skip-gram algorithm
- Implement noise contrastive estimation (NCE)
- Add configurable number of negative samples
- [ ] **Add learning rate scheduling**
- Implement decay schedules (exponential, step, cosine)
- Add learning rate warmup
- Include early stopping based on validation loss
### 2. Enhanced Text Processing
- [ ] **Advanced tokenization**
- Handle compound words and multi-word expressions
- Add subword tokenization (BPE, WordPiece)
- Support for unicode normalization
- [ ] **Text cleaning pipeline**
- Remove HTML tags and URLs
- Handle contractions and possessives
- Number and date normalization
- [ ] **Language support**
- Support for non-English text
- Language detection and processing
- Unicode text handling improvements
## Medium Priority 🟡
### 3. Model Improvements
- [ ] **Advanced architectures**
- Implement GloVe algorithm
- Add FastText character-level embeddings
- Support for transformer-based embeddings (BERT, RoBERTa)
- [ ] **Regularization techniques**
- Add L2 regularization
- Implement dropout for neural network variants
- Add weight decay and momentum
- [ ] **Embedding normalization**
- L2 normalization of embeddings
- Power normalization for better clustering
- Centering and whitening options
### 4. Performance & Optimization
- [ ] **GPU acceleration**
- Implement CUDA backend using candle
- Add OpenCL support
- Optimize for batch processing
- [ ] **Memory optimization**
- Implement lazy loading for large datasets
- Add memory-mapped file support
- Optimize vocabulary storage
- [ ] **Training optimization**
- Implement mini-batch processing
- Add gradient clipping
- Mixed precision training
### 5. Evaluation & Validation
- [ ] **Evaluation metrics**
- Implement standard word similarity benchmarks
- Add analogy accuracy testing
- Include downstream task evaluation
- [ ] **Validation framework**
- Create train/validation split
- Implement cross-validation
- Add learning curve visualization
- [ ] **Quality assessment**
- Embedding quality scoring
- Cluster analysis tools
- Visualization capabilities
### 6. CLI & Library Enhancements
- [ ] **Advanced CLI features**
- Interactive training mode
- Progress bars and logging
- Configuration file support (YAML/TOML)
- [ ] **Library extensions**
- Add streaming training for large datasets
- Implement incremental training
- Support for pre-trained embeddings loading
## Low Priority 🟢
### 7. Documentation & Testing
- [ ] **Comprehensive documentation**
- API documentation with examples
- Tutorial for different use cases
- Research background and theory explanations
- [ ] **Extended testing**
- Property-based testing
- Fuzzing for edge cases
- Integration tests for real-world scenarios
- [ ] **Performance benchmarks**
- Compare with existing implementations (Word2Vec, GloVe)
- Benchmark on different datasets
- Memory and speed profiling
### 8. Additional Features
- [ ] **Multi-modal embeddings**
- Support for combining text and image embeddings
- Cross-modal similarity search
- [ ] **Real-time processing**
- Online learning capabilities
- Incremental vocabulary updates
- Streaming similarity search
- [ ] **Export formats**
- Support for TensorFlow/PyTorch formats
- Add ONNX export
- Custom format specifications
### 9. Community & Integration
- [ ] **Package distribution**
- Publish to crates.io
- Create Docker containers
- Add CI/CD pipeline
- [ ] **Plugin system**
- Custom embedding architectures
- Extensible tokenizers
- Plugin evaluation framework
- [ ] **Language bindings**
- Python wrapper
- Node.js bindings
- C interface for integration
## Research & Experimental 🔬
### 10. Advanced Research
- [ ] **Contextual embeddings**
- Implement dynamic embeddings that change based on context
- Support for sentence-level embeddings
- Document embeddings
- [ ] **Multi-lingual embeddings**
- Cross-lingual similarity
- Language detection integration
- Zero-shot transfer learning
- [ ] **Domain-specific embeddings**
- Medical terminology processing
- Legal document embeddings
- Technical domain adaptation
### 11. Experimental Features
- [ ] **Semantic search**
- Implement approximate nearest neighbor search
- Add hierarchical clustering
- Support for query expansion
- [ ] **Embedding manipulation**
- Word arithmetic (king - man + woman = queen)
- Embedding interpolation
- Semantic vector operations
## Maintenance 🛠️
### 12. Maintenance Tasks
- [ ] **Dependency updates**
- Keep dependencies up to date
- Monitor security advisories
- Test compatibility updates
- [ ] **Performance monitoring**
- Regular benchmarking
- Memory usage tracking
- Profile optimization opportunities
- [ ] **Code quality**
- Regular refactoring
- Code review process
- Static analysis integration
## Completion Criteria ✅
- [ ] Core training algorithm produces meaningful embeddings
- [ ] All tests passing with 100% coverage
- [ ] Performance benchmarks meet or exceed Word2Vec/GloVe
- [ ] Comprehensive documentation and examples
- [ ] CLI interface fully functional with all commands working
- [ ] Library API stable and well-documented
- [ ] Multi-platform support (Linux, macOS, Windows)
- [ ] Integration with popular machine learning frameworks
---
**Last Updated**: 2026-06-09
**Priority Level**: High - Core functionality needs immediate attention
**Estimated Completion**: 2-4 weeks for core features, ongoing for improvements