# 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)
- [x] WordPiece subword tokenization (`WordPieceTokenizer` with greedy longest-match)
- 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`)
- [x] Transformer encoder with multi-head self-attention and position encoding (`TransformerEncoder`)
- GloVe algorithm (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**
- [x] Backend abstraction trait (`Backend`, `CpuBackend`) with pluggable architecture
- [x] wgpu compute shader backend (`GpuBackend`) with `matmul`, `dot`, `add_scaled`
- [x] Feature-gated via `gpu` flag; auto-falls back to CPU if no GPU available
- Works on Vulkan, Metal, DX12 without vendor-specific SDKs
- [x] **Memory optimization**
- [x] Streaming sentence iterator (`DataLoader::stream_sentences`)
- [x] Memory-mapped embedding files (`MmapEmbeddings` with `.bin` format)
- 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()`)
- [x] Standard word similarity benchmarks (`BenchmarkEvaluator` with Spearman correlation, TSV loading)
- [x] **Validation framework**
- [x] Train/validation split (`split_data()`)
- [x] CLI `--validation-ratio` flag for automatic train/val split
- [x] CLI `validate` command for evaluating saved models on new data
- [x] Validation metrics output (accuracy, precision, recall, f1, mean similarity, quality score)
- [x] Optional validation metrics JSON export
- [x] Cross-validation (`cross_validate` with k-fold split, averaged and per-fold metrics)
- [x] Learning curve visualization (`TrainingHistory` with per-epoch loss/LR, JSON export)
- [x] **Quality assessment**
- [x] Embedding quality scoring (`calculate_embedding_quality()`)
- [x] L2 normalization verification (`normalize_embeddings` + unit-norm tests)
- [x] Cluster analysis tools (`KMeansClustering` with centroid-based grouping)
- 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] Pre-trained embeddings loading (`new_with_pretrained` from Word2Vec format)
- [x] `PretrainedEmbeddings` / `PretrainedLoader` with format auto-detection
- [x] Word2Vec text format (`.txt`)
- [x] Word2Vec binary format (Google `.bin`)
- [x] GloVe text format
- [x] fastText `.vec` text format
- [x] Memory-mapped `.bin` format
- [x] Cosine similarity and top-k most similar lookup on pretrained sets
- [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, weighted average, attention fusion, projection fusion, and cross-modal similarity)
- 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] Real-time incremental training (`IncrementalTrainer::update` and `stream_train`)
- [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
## Future Enhancements 🚀
### 13. Training Improvements
- [x] **Negative sampling distribution**
- [x] Unigram distribution raised to 3/4 power (Mikolov et al.)
- [x] Enabled by default (`use_unigram_negative_sampling: true`)
- [x] Falls back to uniform random if word frequencies unavailable
- [x] **Sub-sampling of frequent words**
- [x] Word2Vec-style `P(w) = 1 - sqrt(t / f(w))` for words above threshold
- [x] Configurable via `subsample_threshold` (opt-in, typical: `1e-5`)
- [x] Benefits: faster training, better representation of rare words
- [x] **Learning rate warm-up**
- [x] Linear warm-up for first N epochs before main schedule
- [x] Configurable via `warmup_epochs` (opt-in, `None` disables)
- [x] Benefits: stabler early training, especially with large batch sizes
- [x] **Model checkpointing**
- [x] Save intermediate checkpoints every N epochs (`checkpoint_interval`)
- [x] Resume training from checkpoint (`load_checkpoint`)
- [x] Configurable output directory (`checkpoint_path`)
- [x] **Multi-threaded / parallel training**
- [x] Parallelize sentence processing over CPU cores via `rayon`
- [x] Thread-local gradient accumulators merged at epoch end
- [x] Opt-in via `use_parallel` flag
### 14. Inference & Deployment
- [ ] **INT8 / FP16 quantization**
- Post-training quantization for smaller model sizes (4x smaller for INT8)
- Quantization-aware training option
- Export quantized ONNX
- [ ] **HNSW approximate nearest neighbor index**
- Replace LSH with Hierarchical Navigable Small World graphs
- Benefits: significantly higher recall at same latency, supports billion-scale
- [ ] **Built-in benchmark datasets**
- Ship WordSim-353, SimLex-999, MEN, RW, SCWS as embedded TSVs
- `BenchmarkEvaluator::load_builtin("wordsim353")` convenience API
- [ ] **OOV (Out-of-Vocabulary) fallback**
- Subword composition: average of character n-gram embeddings for unknown words
- FastText-style character n-gram bucket embeddings
### 15. Advanced Models
- [ ] **Contrastive sentence embeddings (SimCSE-style)**
- Dropout-based positive pairs + in-batch negatives
- Better sentence representations than simple mean-pooling
- [ ] **Word sense disambiguation**
- Multiple prototype vectors per word (cluster contexts into senses)
- Context-aware sense selection at lookup time
- [ ] **Streaming vocabulary building**
- Build vocabulary from files larger than RAM without loading all sentences
- Reservoir-sampling-based vocab estimation
- [ ] **Automatic hyperparameter search**
- Grid search or Bayesian optimization over `dim`, `lr`, `window`, `negative_samples`
- Built-in cross-validation scoring as objective function
### 16. Developer Experience
- [ ] **Model comparison / diff tool**
- Compare two embedding files (cosine alignment, vocabulary overlap, nearest neighbor overlap)
- [ ] **Embedding projector export**
- Export to TensorBoard projector format (TSV + metadata) for visualization
- [ ] **Python bindings (PyO3)**
- `embedding` Python package exposing core training and inference
- NumPy array interop for zero-copy embedding access
## 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 (126 tests: 86 unit + 35 integration + 5 doc-tests, 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)
---
**Last Updated**: 2026-06-13 (v0.1.5 published)
**Priority Level**: Core features complete; v2.0 research features in progress
**Estimated Completion**: Core features complete; enhancements in Future Enhancements section