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v1.3.0
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@ozancaglayan ozancaglayan released this 30 Apr 12:33
· 594 commits to master since this release
v1.3.0
684e90a
  • Added Multi30kRawDataset for training end-to-end systems from raw images as input.
  • Added NumpyDataset to read .npy/.npz tensor files as input features.
  • You can now pass -S to nmtpy train to produce shorter experiment files with not all the hyperparameters in file name.
  • New post-processing filter option de-spm for Google SentencePiece (SPM) processed files.
  • sacrebleu is now a dependency as it is now accepted as an early-stopping metric.
    It only makes sense to use it with SPM processed files since they are detokenized
    once post-processed.
  • Added sklearn as a dependency for some metrics.
  • Added momentum and nesterov parameters to [train] section for SGD.
  • ImageEncoder layer is improved in many ways. Please see the code for further details.
  • Added unmerged upstream PR for ModuleDict() support.
  • METEOR will now fallback to English if language can not be detected from file suffixes.
  • -f now produces a separate numpy file for token frequencies when building vocabulary files with nmtpy-build-vocab.
  • Added new command nmtpy test for non beam-search inference modes.
  • Removed nmtpy resume command and added pretrained_file option for [train] to initialize model weights from a checkpoint.
  • Added freeze_layers option for [train] to give comma-separated list of layer name prefixes to freeze.
  • Improved seeding: seed is now printed in order to reproduce the results.
  • Added IPython notebook for attention visualization.
  • Layers
    • New shallow SimpleGRUDecoder layer.
    • TextEncoder: Ability to set maxnorm and gradscale of embeddings and work with or without sorted-length batches.
    • ConditionalDecoder: Make it work with GRU/LSTM, allow setting maxnorm/gradscale for embeddings.
    • ConditionalMMDecoder: Same as above.
  • nmtpy translate
    • --avoid-double and --avoid-unk removed for now.
    • Added Google's length penalty normalization switch --lp-alpha.
    • Added ensembling which is enabled automatically if you give more than 1 model checkpoints.
  • New machine learning metric wrappers in utils/ml_metrics.py:
    • Label-ranking average precision lrap
    • Coverage error
    • Mean reciprocal rank
Assets 2