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Changelog

1.0.3

  • Fixed regression in RandomBaseline (#94)
  • Added TensorBoard Embedding Projector support (#86)
  • Minor bugfixing (#87, #47)

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
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