def predict(request: GrammarRequest, model: GecBERTModel = Depends(get_model)):
# return model.predict(request)
preds, _ = model.handle_batch([request.text.split()])
if len(preds) > 0:
result = ""
for word in preds[0]:
remove_characters = ["'", '"', "(", ")", "[", "]"]
for character in remove_characters:
word = word.replace(character, "")
first_char = word[0]
if first_char.isalnum():
result += " "
result += word
return GrammarResponse(result=result.strip())
else:
return GrammarResponse(result="")
class GectorBertModel(lit_model.Model):
ATTENTION_LAYERS = 12
ATTENTION_HEADS = 12
MAX_LEN = 50
def __init__(self, model_path):
self.model = GecBERTModel(
vocab_path='data/output_vocabulary',
model_paths=[model_path],
max_len=GectorBertModel.MAX_LEN,
min_len=3,
iterations=
1, # limit to 1 iteration to make attention analysis reasonable
min_error_probability=0.0,
model_name='bert', # we're using BERT
special_tokens_fix=0, # disabled for BERT
log=False,
confidence=0,
is_ensemble=0,
weigths=None)
# LIT API implementation
def max_minibatch_size(self, config: Any = None) -> int:
return 32
def predict_minibatch(self, inputs: List, config=None) -> List:
# we append '$START' to the beginning of token lists because GECTOR does as well (and this is what BERT
# ends up processing. see preprocess() and postprocess_batch() in gec_model
# this breaks down if we have duplicates, but we shouldn't
sentence_indices = [(ex["input_text"], index)
for index, ex in enumerate(inputs)]
tokenized_input_with_indices = [
(original.split(), index) for original, index in sentence_indices
]
batch = [(tokens, index)
for tokens, index in tokenized_input_with_indices
if len(tokens) >= self.model.min_len]
# anything under min length doesn't get processed anyway, so we don't pass it in and just keep it
# so we can put stuff back in order later
ignored = [(tokens, index)
for tokens, index in tokenized_input_with_indices
if len(tokens) < self.model.min_len]
model_input = [tokens for tokens, index in batch]
predictions, _, attention = self.model.handle_batch(model_input)
attention = attention[0] # we only have one iteration
assert (len(predictions) == len(attention))
output = [{
'predicted': ' '.join(tokenlist)
} for tokenlist in predictions]
# wanted to average across heads and layers, but attention with different head counts breaks LIT
#attention_averaged = numpy.average(attention, (1, 2))[:, numpy.newaxis, ...]
batch_iter = iter(batch)
for output_dict, attention_info in zip(output, attention):
original_tokens, original_index = next(batch_iter)
output_dict['original_index'] = original_index
output_dict['layer_average'] = numpy.average(attention_info,
axis=0)
for layer_index, attention_layer_info in enumerate(attention_info):
output_dict['layer{}'.format(
layer_index)] = attention_layer_info
output.extend({
'predicted': ' '.join(tokens),
'original_index': original_index
} for tokens, original_index in ignored)
output.sort(key=lambda x: x['original_index'])
for tokenized_input, index in tokenized_input_with_indices:
output[index]['input_tokens'] = ['$START'] + tokenized_input
for d in output:
del d['original_index']
return output
def input_spec(self) -> lit_types.Spec:
return {
"input_text": lit_types.TextSegment(),
"target_text": lit_types.TextSegment()
}
def output_spec(self) -> lit_types.Spec:
output = {
"input_tokens":
lit_types.Tokens(parent="input_text"),
"predicted":
lit_types.GeneratedText(parent='target_text'),
'layer_average':
lit_types.AttentionHeads(align=('input_tokens', 'input_tokens'))
}
for layer in range(self.ATTENTION_LAYERS):
output['layer{}'.format(layer)] = lit_types.AttentionHeads(
align=('input_tokens', 'input_tokens'))
return output
class GrammarCorrection:
def __init__(self):
"""
vocab_path: Path to the vocab file.
model_paths: Path to the model file.
max_len: The max sentence length(all longer will be truncated). type->int
min_len: The minimum sentence length(all longer will be returned w/o changes) type-> int
iterations: The number of iterations of the model. type->int
min_error_probability: Minimum probability for each action to apply.
Also, minimum error probability, as described in the paper. type->float
lowercase_tokens: The number of iterations of the model. type->int
model_name: Name of the transformer model.you can chose['bert', 'gpt2', 'transformerxl', 'xlnet', 'distilbert', 'roberta', 'albert']
special_tokens_fix: Whether to fix problem with [CLS], [SEP] tokens tokenization.
For reproducing reported results it should be 0 for BERT/XLNet and 1 for RoBERTa. type->int
confidence: How many probability to add to $KEEP token. type->float
is_ensemble: Whether to do ensembling. type->int
weigths: Used to calculate weighted average.
batch_size: The size of hidden unit cell.
:return:
"""
if not os.path.exists('./model/bert_0_gector.th'):
self.download_model()
self.model = GecBERTModel(vocab_path='./data/output_vocabulary',
model_paths=['./model/bert_0_gector.th'],
max_len=50, min_len=3,
iterations=5,
min_error_probability=0.0,
lowercase_tokens=0,
model_name='bert',
special_tokens_fix=0,
log=False,
confidence=0,
is_ensemble=0,
weigths=None)
@staticmethod
def download_model():
link = "http://imdreamer.oss-cn-hangzhou.aliyuncs.com/bert_0_gector.th"
with open('./model/bert_0_gector.th', "wb") as f:
print('Downloading grammatical error correction model [bert_0_gector]! Please wait a minute!')
response = requests.get(link, stream=True)
total_length = response.headers.get('content-length')
if total_length is None: # no content length header
f.write(response.content)
else:
dl = 0
total_length = int(total_length)
for data in response.iter_content(chunk_size=4096):
dl += len(data)
f.write(data)
done = int(50 * dl / total_length)
sys.stdout.write("\r[%s%s]" % ('=' * done, ' ' * (50 - done)))
sys.stdout.flush()
@staticmethod
def language_checker(text):
matches = tool.check(text)
correct_text = lc.correct(text, matches)
return correct_text
def correct_sentence_by_file(self, input_file='./data/predict_for_file/input.txt',
output_file='./data/predict_for_file/output.txt', batch_size=32):
test_data = read_lines(input_file)
predictions = []
cnt_corrections = 0
batch = []
for sent in test_data:
batch.append(self.language_checker(sent).split())
if len(batch) == batch_size:
preds, cnt = self.model.handle_batch(batch)
predictions.extend(preds)
cnt_corrections += cnt
batch = []
if batch:
preds, cnt = self.model.handle_batch(batch)
predictions.extend(preds)
cnt_corrections += cnt
with open(output_file, 'w') as f:
f.write("\n".join([" ".join(x) for x in predictions]) + '\n')
return cnt_corrections
def correct_sentence(self, input_string):
predictions = []
cnt_corrections = 0
batch = [self.language_checker(input_string).split()]
if batch:
preds, cnt = self.model.handle_batch(batch)
predictions.extend(preds)
cnt_corrections += cnt
return [" ".join(x) for x in predictions][0]