post_process: Optional[Callable[[str], str]] = None) -> List[str]:
"""Reads the lines in the file specified in loc_ and return the list after inserting the tokens
"""
data = list()
with open(loc_, encoding='utf-8') as fp:
for line in fp:
t = line.strip()
if post_process is not None:
t = post_process(t)
data.append(t)
return data
eval_shortcuts = {
"bleu": lambda: metrics.BLEUEvaluator(),
"gleu": lambda: metrics.GLEUEvaluator(),
"wer": lambda: metrics.WEREvaluator(),
"cer": lambda: metrics.CEREvaluator(),
"recall": lambda: metrics.RecallEvaluator(),
"accuracy": lambda: metrics.SequenceAccuracyEvaluator(),
"fmeasure": lambda: metrics.FMeasureEvaluator(),
"seg_fmeasure": lambda: metrics.SegmentationFMeasureEvaluator(),
"rnng_parse_fmeasure": lambda: metrics.RNNGParseFMeasureEvaluator(),
}
def xnmt_evaluate(ref_file: Union[str, Sequence[str]],
hyp_file: Union[str, Sequence[str]],
evaluators: Sequence[metrics.Evaluator],
desc: Any = None) -> Sequence[metrics.EvalScore]:
""""Returns the eval score (e.g. BLEU) of the hyp sents using reference trg sents
def setUp(self):
self.evaluator = metrics.GLEUEvaluator()