- PR #155: Faster reading of triple files
- d275419, 87c5463: Support parameter groups with group-specific optimizer args
- PR #152: Added training loss evaluation job
- PR #147: Support both minimization and maximization of metrics
- PR #144: Support to tune subbatch size automatically
- PR #143: Allow processing of large batches in smaller subbatches to save memory
- PR #140: Calculate penalty for entities only once, if subject-embedder == object-embedder
- PR #138: Revision of hooks, fix of embedding normalization
- PR #135: Revised sampling API, faster negative sampling with shared samples
- PR #112: Initialize embeddings from a packaged model
- PR #113: Reduce memory consumption and loading times of large datasets
- Various smaller improvements and bug fixes
- PR #110: Support for different tie-breaking methods in evaluation (thanks Nzteb)
- 1d26e63: Add head/tail evaluation per relation type
- dfd0aac: Added squared error loss (thanks Nzteb)
- PR #104: Fix incorrect relation type measurement (thanks STayinloves)
- PR #101: Revise embedder penalty API (thanks Nzteb)
- PR #94: Support for packaged models (thanks AdrianKS)
- Improved seeding of workers when a fixed NumPy seed is used
- Various smaller improvements and bug fixes
- Added more mappings from entity IDs to names for Freebase datasets (in entity_strings.del file)
- Improved shared negative sampling (WOR sampling, exclude positive triples from negative sample)
- PR #86: Support (s,?,o) queries for KvsAll training (thanks vonVogelstein)
- cf64dd2: Fast dataset/index loading via cached pickle files
- 4bc86b1: Add support for chunking a batch when training with negative sampling
- 14dc926: Add ability to dump configs in various ways
- PR #64: Initial support for frequency-based negative sampling (thanks AdrianKS)
- PR #77: Simpler use of command-line interface (thanks cthoyt)
- 76a0077: Added RotatE
- 7235e99: Added option to add a constant offset before computing BCE loss
- 67de6c5: Added CP
- a5ee441: Added SimplE
- PR #71: Faster and more memory-efficient training with negative sampling (thanks AdrianKS)
- Initial release