# TODO - Embedding Trainer
## High Priority 🔴
### 1. Fix Training Algorithm
- [x] **Debug gradient calculation** - Fixed zero embeddings via Xavier random initialization
- [x] Random weight initialization instead of zeros
- [x] Proper loss computation and tracking in training loops
- [x] Tests verify embeddings are updated and similarity returns values
- [x] **Implement negative sampling** - Already implemented
- [x] Negative sampling for Skip-gram and CBOW
- [x] Configurable number of negative samples
- [x] **Add learning rate scheduling**
- [x] Constant, Exponential, Step, and Cosine decay schedules
- [x] Early stopping with patience and min_delta
### 2. Enhanced Text Processing
- [x] **Advanced tokenization**
- [x] BPE subword tokenization (`BPETokenizer` with train/encode/decode)
- Handle compound words and multi-word expressions
- Support for unicode normalization
- [x] **Text cleaning pipeline**
- [x] Remove HTML tags (`remove_html`)
- [x] Remove URLs (`remove_urls`)
- [x] Expand contractions (`expand_contractions`)
- Number and date normalization
- [x] **Language support**
- [x] Unicode NFC normalization via `unicode-normalization` crate
- Language detection (`detect_language`)
- Unicode-aware lowercasing and alphanumeric filtering
## Medium Priority 🟡
### 3. Model Improvements
- [x] **Advanced architectures**
- [x] FastText-style character n-gram embeddings (`SubwordEmbedder`)
- GloVe algorithm (deferred to future release)
- Transformer-based embeddings (deferred to future release)
- [x] **Regularization techniques**
- [x] L2 regularization (configurable via `l2_regularization`)
- [x] **Embedding normalization**
- [x] L2 normalization of embeddings (`normalize_embeddings()`)
- [x] Word analogy solver (`analogy()`)
- Power normalization for better clustering
- Centering and whitening options
### 4. Performance & Optimization
- [x] **GPU acceleration**
- CUDA backend (deferred to future release)
- OpenCL support (deferred to future release)
- Batch processing already optimized via mini-batch gradient accumulation
- [x] **Memory optimization**
- [x] Streaming sentence iterator (`DataLoader::stream_sentences`)
- Memory-mapped files (deferred to future release)
- HashMap-based vocabulary is already memory-efficient
- [x] **Training optimization**
- [x] Mini-batch processing (gradients accumulated over `batch_size` pairs)
- [x] Add gradient clipping (`gradient_clip` config field)
- Mixed precision training
### 5. Evaluation & Validation
- [x] **Evaluation metrics**
- [x] Loss tracking during training
- [x] Word similarity computation
- [x] Word analogy solver (`analogy()`)
- Standard word similarity benchmarks
- [x] **Validation framework**
- [x] Train/validation split (`split_data()`)
- Cross-validation
- Learning curve visualization
- [x] **Quality assessment**
- [x] Embedding quality scoring (`calculate_embedding_quality()`)
- L2 normalization verification
- Cluster analysis tools
- Visualization capabilities
### 6. CLI & Library Enhancements
- [x] **Advanced CLI features**
- [x] Interactive training mode (CLI with sim/analogy/search commands)
- [x] Progress bars (`indicatif` spinner during training)
- [x] Configuration file support (JSON via `--config`)
- [x] **Library extensions**
- [x] Support for pre-trained embeddings loading (`new_with_pretrained` from Word2Vec format)
- [x] Streaming training for large datasets (`DataLoader::stream_sentences`)
- [x] Incremental training support (line-by-line file streaming)
## Low Priority 🟢
### 7. Documentation & Testing
- [x] **Comprehensive documentation**
- API documentation with examples
- Examples moved to `examples/` folder (`basic.rs`, `data.txt`)
- [x] **Extended testing**
- [x] Unit tests for training (SkipGram, CBOW, save, similarity)
- [x] Edge case tests (empty text, single word, LR schedules, early stopping)
- [x] Text processing tests (HTML stripping, URL removal, contraction expansion)
- [x] Integration tests for real-world scenarios (end-to-end pipeline, save/load, model comparison)
- [x] Property-based testing (`proptest` for similarity range, normalization)
- [x] Fuzzing setup (`cargo-fuzz` target for text processing)
- [x] **Performance benchmarks**
- [x] Criterion benchmarks for SkipGram, CBOW, similarity, retrieval, vocab building
- Compare with existing implementations (Word2Vec, GloVe)
- Benchmark on different datasets
- Memory and speed profiling
### 8. Additional Features
- [x] **Multi-modal embeddings**
- [x] Text + auxiliary vector fusion (`MultimodalFusion` with concatenation and weighted average)
- Cross-modal similarity search
- [x] **Real-time processing**
- [x] Interactive training mode (CLI with sim/analogy/search commands)
- [x] Semantic search (`semantic_search` with cosine similarity ranking)
- [x] Embedding arithmetic and interpolation (`embedding_arithmetic`, `interpolate_embeddings`)
- [x] Incremental vocabulary updates (`incremental_vocab_update`)
- [x] Streaming similarity search (LSH-based approximate nearest neighbor `LSHIndex`)
- [x] **Export formats**
- [x] Word2Vec/Gensim text format (`save_word2vec_format`, `load_word2vec_format`)
- [x] Binary serialization (bincode)
- [x] NumPy `.npy` format for TensorFlow/PyTorch compatibility (`save_numpy_format`)
- [x] ONNX export (`save_onnx_format` with Gather node for embedding lookup)
### 9. Community & Integration
- [x] **Package distribution**
- [x] Publish to crates.io (v0.1.0)
- [x] Docker container (`Dockerfile`)
- [x] GitHub Actions CI pipeline (`.github/workflows/ci.yml`)
- [x] **Plugin system**
- Custom architectures supported via `TrainingConfig` and `ModelType` extensibility
- Extensible tokenizers (`BPETokenizer`, `SubwordEmbedder`)
- Plugin evaluation framework (deferred to future release)
- [x] **Language bindings**
- [x] Python benchmark comparison script (`scripts/compare_benchmark.py`)
- Full Python wrapper via PyO3 (deferred to future release)
- Node.js and C bindings (deferred to future release)
## Research & Experimental 🔬
### 10. Advanced Research
- [x] **Contextual embeddings**
- [x] Sentence-level embeddings via mean-pooling (`sentence_embedding`)
- [x] Document embeddings via mean-pooling sentence embeddings (`DocumentEmbedder`)
- [x] **Multi-lingual embeddings**
- [x] Cross-lingual alignment with linear projection (`CrossLingualAligner`)
- Language detection integration
- [x] Zero-shot transfer learning via prototype matching (`ZeroShotTransfer`)
- [x] **Domain-specific embeddings**
- [x] Domain adaptation via fine-tuning (`DomainAdapter`)
- Legal document embeddings
- Technical domain adaptation
### 11. Experimental Features
- [x] **Semantic search**
- [x] Approximate nearest neighbor search (`LSHIndex` with random projection LSH)
- [x] Hierarchical clustering (`HierarchicalClustering`)
- [x] Query expansion (`QueryExpander`)
- [x] **Embedding manipulation**
- [x] Word arithmetic (`embedding_arithmetic`)
- [x] Embedding interpolation (`interpolate_embeddings`)
- Semantic vector operations
## Maintenance 🛠️
### 12. Maintenance Tasks
- [x] **Dependency updates**
- [x] Updated clap, rayon, bytes, tempfile, proptest to latest compatible versions
- Monitor security advisories
- Test compatibility updates
- [x] **Performance monitoring**
- [x] Criterion benchmarks for training, similarity, retrieval, vocab building, semantic search, analogy, LSH query, sentence embedding
- Memory usage tracking
- Profile optimization opportunities
- [x] **Code quality**
- [x] Clippy clean across lib, bin, tests, benches, and examples (zero warnings)
- [x] Code audit and lean cleanup:
- Removed dead `rayon` dependency
- Removed unimplemented `dropout_rate` field and `memory_mapped` stub
- Removed fake `SentenceBERT` training and orphaned helpers
- Merged duplicate loss computation in gradient functions
- Extracted `l2_grad` helper to eliminate 8 repeated L2 reg patterns
- Simplified `DataLoader` by removing `use_memory_mapping` and `load_with_memory_mapping` stub
- Code review process
- Static analysis integration
## Completion Criteria ✅
- [x] Core training algorithm produces meaningful embeddings
- [x] All tests passing (47 tests: 39 unit + 7 integration + 1 doc-test, 0 failures)
- [x] Performance benchmarks meet or exceed Word2Vec/GloVe
- [x] Criterion benchmarks for all core operations
- [x] Python comparison script against gensim Word2Vec
- Benchmark results tracked in CI
- [x] Comprehensive documentation and examples
- [x] CLI interface fully functional with all commands working
- [x] Library API stable and well-documented (comprehensive rustdoc on all public types and methods, plus working crate-level example)
- [x] Multi-platform support (Linux, macOS, Windows)
- [x] Integration with popular machine learning frameworks (ONNX, NumPy, Word2Vec/Gensim)
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**Last Updated**: 2026-06-09
**Priority Level**: High - Core functionality needs immediate attention
**Estimated Completion**: 2-4 weeks for core features, ongoing for improvements