def prepare_translators(langspecf):
global translatorbest, translatorbigram, langspec
with open(os.path.join(dir_path, 'opt_data'), 'rb') as f:
opt = pickle.load(f)
if not langspec or langspec != langspecf:
opt.models = [os.path.join(dir_path, 'model', langspecf['model'])]
opt.n_best = 1
ArgumentParser.validate_translate_opts(opt)
logger = init_logger(opt.log_file)
translatorbest = build_translator(opt, report_score=True)
opt.models = [os.path.join(dir_path, 'model', langspecf['model'])]
opt.n_best = 5
opt.max_length = 2
ArgumentParser.validate_translate_opts(opt)
logger = init_logger(opt.log_file)
translatorbigram = build_translator(opt, report_score=True)
langspec = langspecf
langspecs = {
key: value
for key, value in enginedata.items() if value['active']
}
with open(os.path.join(dir_path, 'opt_data'), 'rb') as f:
opt = pickle.load(f)
engines = {}
for key, value in langspecs.items():
opt.models = [os.path.join(dir_path, 'model', value['model'])]
opt.n_best = 1
opt.max_length = 100
opt.global_attention_function = 'sparsemax'
ArgumentParser.validate_translate_opts(opt)
engines[key] = {"translatorbest": build_translator(opt, report_score=True)}
#translatorbest builds the best complete translation of the sentence
opt.n_best = 5
opt.max_length = 2
opt.global_attention_function = 'sparsemax'
ArgumentParser.validate_translate_opts(opt)
engines[key]["translatorbigram"] = build_translator(opt, report_score=True)
#translatorbiagram builds best translations of length two
if value['src_bpe']:
print("BPE in SRC side")
bpe_src_code = os.path.join(dir_path, 'model', value['src_bpe'])
merge_file = open(bpe_src_code, "r")
bpe = apply_bpe.BPE(codes=merge_file)
engines[key]["src_segmenter"] = lambda x: bpe.process_line(x.strip())