v1.3.0

• 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