def get_prediction_on_turkcorpus(simplifier, phase):
source_filepath = get_data_filepath('turkcorpus', phase, 'complex')
pred_filepath = get_temp_filepath()
print(pred_filepath)
with mute():
simplifier(source_filepath, pred_filepath)
return pred_filepath
def transform(input_text,
LengthRatioProcessor=0.95,
LevenshteinPreprocessor=0.75,
WordRankRatioPreprocessor=0.75,
SentencePiecePreprocessor=10000):
input_lines = input_text.split("\n")
# Read from input
source_filepath = get_temp_filepath()
write_lines([word_tokenize(line) for line in input_lines], source_filepath)
# Load best model
best_model_dir = prepare_models()
recommended_preprocessors_kwargs = {
'LengthRatioPreprocessor': {
'target_ratio': LengthRatioProcessor
},
'LevenshteinPreprocessor': {
'target_ratio': LevenshteinPreprocessor
},
'WordRankRatioPreprocessor': {
'target_ratio': WordRankRatioPreprocessor
},
'SentencePiecePreprocessor': {
'vocab_size': SentencePiecePreprocessor
},
}
preprocessors = get_preprocessors(recommended_preprocessors_kwargs)
simplifier = get_fairseq_simplifier(best_model_dir, beam=1)
simplifier = get_preprocessed_simplifier(simplifier,
preprocessors=preprocessors)
# Simplify
pred_filepath = get_temp_filepath()
with mute():
simplifier(source_filepath, pred_filepath)
return list(yield_lines(pred_filepath))
from access.preprocessors import get_preprocessors
from access.resources.prepare import prepare_models
from access.simplifiers import get_fairseq_simplifier, get_preprocessed_simplifier
from access.text import word_tokenize
from access.utils.helpers import yield_lines, write_lines, get_temp_filepath, mute
if __name__ == '__main__':
# Usage: python generate.py < my_file.complex
# Read from stdin
source_filepath = get_temp_filepath()
write_lines([word_tokenize(line) for line in fileinput.input()], source_filepath)
# Load best model
best_model_dir = prepare_models()
recommended_preprocessors_kwargs = {
'LengthRatioPreprocessor': {'target_ratio': 0.95},
'LevenshteinPreprocessor': {'target_ratio': 0.75},
'WordRankRatioPreprocessor': {'target_ratio': 0.75},
'SentencePiecePreprocessor': {'vocab_size': 10000},
}
preprocessors = get_preprocessors(recommended_preprocessors_kwargs)
simplifier = get_fairseq_simplifier(best_model_dir, beam=8)
simplifier = get_preprocessed_simplifier(simplifier, preprocessors=preprocessors)
# Simplify
pred_filepath = get_temp_filepath()
with mute():
simplifier(source_filepath, pred_filepath)
for line in yield_lines(pred_filepath):
print(line)