kiwi.runner

Module Contents

Classes

Predictions

Runner

kiwi.runner.logger
class kiwi.runner.Predictions
sentences_hter :List[float]
target_tags_BAD_probabilities :List[List[float]]
target_tags_labels :List[List[str]]
source_tags_BAD_probabilities :List[List[float]]
source_tags_labels :List[List[str]]
gap_tags_BAD_probabilities :List[List[float]]
gap_tags_labels :List[List[str]]
class kiwi.runner.Runner(system: QESystem, device: Optional[int] = None)
configure_outputs(self, output_config: QEOutputs.Config)
static wrap_predictions(predictions: Dict[str, List[Any]]) → Predictions
predict(self, source: List[str], target: List[str], alignments: List[str] = None, batch_size: int = 1, num_data_workers: int = 0) → Predictions

Create predictions for a list of examples.

Parameters

  • source – a list of sentences on a source language.

  • target – a list of (translated) sentences on a target language.

  • alignments – optional list of source-target alignments required only by the NuQE model.

  • batch_size – how large to build a batch (default: 1).

  • num_data_workers – how many subprocesses to use for data loading.

Returns

A Predictions object with predicted outputs for each example in the inputs. If input source and target are all empty, returned object has all attributes as None. If there are aligned empty sentences at both source and target, the corresponding returned prediction will contain empty/zero values (empty list for word level outputs, 0.0 for sentence level outputs).

Raises

Exception –

If an example has an empty string as source xor target

field (not both at the same time).

Notes

source and target lenghts must match.

Example

>>> from kiwi.lib import predict
>>> runner = predict.load_system('../tests/toy-data/models/nuqe.ckpt')
>>> source = ['a b c', 'd e f g']
>>> target = ['q w e r', 't y']
>>> alignments = ['0-0 1-1 1-2', '1-1 3-0']
>>> predictions = runner.predict(source, target, alignments)
>>> predictions.target_tags_BAD_probabilities  
[[0.49699464440345764, 0.49956727027893066, ...], [..., 0.5013138651847839]]
Predictions(

sentences_hter=[0.2668147683143616, 0.26675286889076233], target_tags_BAD_probabilities=[

[

0.49699464440345764, 0.49956727027893066, 0.5025501847267151, 0.5057167410850525,

], [0.4967852830886841, 0.5013138651847839],

], target_tags_labels=[[‘OK’, ‘OK’, ‘BAD’, ‘BAD’], [‘OK’, ‘BAD’]], source_tags_BAD_probabilities=None, source_tags_labels=None, gap_tags_BAD_probabilities=[

[

0.42644527554512024, 0.42096763849258423, 0.41709718108177185, 0.4157106280326843, 0.41496342420578003,

], [0.42876192927360535, 0.4251120686531067, 0.4210476577281952],

], gap_tags_labels=[[‘OK’, ‘OK’, ‘OK’, ‘OK’, ‘OK’], [‘OK’, ‘OK’, ‘OK’]],

)

run(self, iterator=None) → Dict[str, List]
static remove_empty_sentences(columns: Dict[str, List[str]]) → Tuple[Dict[str, List[str]], List[int]]
static insert_dummy_outputs_for_empty_sentences(predictions: Dict[str, List], indices_of_empties: List[int]) → Dict[str, List]
kiwi.loggers