3.0.4

[3.0.4]

• Make sure data permutation indices are in int64 format (doesn't seem to be the case by default on all platforms).

[3.0.3]

Fixed

• Fixed ensemble decoding for models without target factors.

[3.0.2]

Changed

• sockeye-translate: Beam search now computes and returns secondary target factor scores. Secondary target factors
  do not participate in beam search, but are greedily chosen at every time step. Accumulated scores for secondary factors
  are not normalized by length. Factor scores are included in JSON output (--output-type json).
• sockeye-score now returns tab-separated scores for each target factor. Users can decide how to combine factor scores
  depending on the downstream application. Score for the first, primary factor (i.e. output words) are normalized,
  other factors are not.

[3.0.1]

Fixed

• Parameter averaging (sockeye-average) now always uses the CPU, which enables averaging parameters from GPU-trained models on CPU-only hosts.