Sockeye

Latest version: v3.1.34

Safety actively analyzes 682487 Python packages for vulnerabilities to keep your Python projects secure.

Scan your dependencies

Page 13 of 45

2.3.3

Changed

- Upgraded `SacreBLEU` dependency of Sockeye to a newer version (`1.4.14`).

2.3.2

Not secure
Fixed

- Fixed edge case that unintentionally skips softmax for sampling if beam size is 1.

2.3.1

Fixed

- Optimizing for BLEU/CHRF with horovod required the secondary workers to also create checkpoint decoders.

2.3.0

Added

- Added support for target factors.
If provided with additional target-side tokens/features (token-parallel to the regular target-side) at training time,
the model can now learn to predict these in a multi-task setting. You can provide target factor data similar to source
factors: `--target-factors <factor_file1> [<factor_fileN>]`. During training, Sockeye optimizes one loss per factor
in a multi-task setting. The weight of the losses can be controlled by `--target-factors-weight`.
At inference, target factors are decoded greedily, they do not participate in beam search.
The predicted factor at each time step is the argmax over its separate output
layer distribution. To receive the target factor predictions at inference time, use
`--output-type translation_with_factors`.

Changed

- `load_model(s)` now returns a list of target vocabs.
- Default source factor combination changed to `sum` (was `concat` before).
- `SockeyeModel` class has three new properties: `num_target_factors`, `target_factor_configs`,
and `factor_output_layers`.

2.2.8

Not secure
Changed
- Make source/target data parameters required for the scoring CLI to avoid cryptic error messages.

2.2.7

Added

- Added an argument to specify the log level of secondary workers. Defaults to ERROR to hide any logs except for exceptions.

Page 13 of 45

Links

Releases

Has known vulnerabilities

© 2024 Safety CLI Cybersecurity Inc. All Rights Reserved.