Changelog¶
1.0.2¶
- Added multiclass loss (#24 and #22)
- Updated the negative generation to speed up evaluation for default protocol.(#74)
- Support for visualization of embeddings using Tensorboard (#16)
- Save models with custom names. (#71)
- Quick fix for the overflow issue for datasets with a million entities. (#61)
- Fixed issues in train_test_split_no_unseen API and updated api (#68)
- Added unit test cases for better coverage of the code(#75)
- Corrupt_sides : can now generate corruptions for training on both sides, or only on subject or object
- Better error messages
- Reduced logging verbosity
- Added YAGO3-10 experiments
- Added MD5 checksum for datasets (#47)
- Addressed issue of ambiguous dataset loaders (#59)
- Renamed ‘type’ parameter in models.get_embeddings to fix masking built-in function
- Updated String comparison to use equality instead of identity.
- Moved save_model and restore_model to ampligraph.utils (but existing API will remain for several releases).
- Other minor issues (#63, #64, #65, #66)
1.0.1¶
- evaluation protocol now ranks object and subjects corruptions separately
- Corruption generation can now use entities from current batch only
- FB15k-237, WN18RR loaders filter out unseen triples by default
- Removed some unused arguments
- Improved documentation
- Minor bugfixing
1.0.0¶
- TransE
- DistMult
- ComplEx
- FB15k, WN18, FB15k-237, WN18RR, YAGO3-10 loaders
- generic loader for csv files
- RDF, ntriples loaders
- Learning to rank evaluation protocol
- Tensorflow-based negatives generation
- save/restore capabilities for models
- pairwise loss
- nll loss
- self-adversarial loss
- absolute margin loss
- Model selection routine
- LCWA corruption strategy for training and eval
- rank, Hits@N, MRR scores functions