def _build_bert_input(self, input_sentences):
# first convert raw text sentences into a list of InputExample objects
input_examples = []
# in this case use single sentence InputExamples for single embedding
# loop through each full example from input df
for idx, sentence in enumerate(input_sentences):
input_example = InputExample(
unique_id = idx,
text_a = sentence,
text_b = None
)
input_examples.append(input_example)
# then convert the InputExample objects to InputFeature objects
input_features = convert_examples_to_features(
examples=input_examples,
seq_length=self.max_seq_length,
tokenizer=self.tokenizer
)
# build input_fn to feed to bert model
input_fn = input_fn_builder(
features=input_features,
seq_length=self.max_seq_length
)
# store required info from each example for use in predict loop
input_features_dict = {}
for feature in input_features:
input_features_dict[feature.unique_id] = feature
return input_fn, input_features_dict
def extract(self, sentence):
example = [InputExample(unique_id=0, text_a=sentence, text_b=None)]
features = convert_examples_to_features(examples=example,
seq_length=_max_seq_length,
tokenizer=self._tokenizer)
input_fn = input_fn_builder(features=features,
seq_length=_max_seq_length)
outputs = []
for output in self._estimator.predict(input_fn):
feature = features[0]
output_dict = collections.OrderedDict()
all_features = []
for (i, token) in enumerate(feature.tokens):
all_layers = []
for (j, layer_index) in enumerate(_layers):
layer_output = output["layer_output_%d" % j]
layers = collections.OrderedDict()
layers["index"] = layer_index
layers["values"] = [
round(float(x), 6)
for x in layer_output[i:(i + 1)].flat
]
all_layers.append(layers)
features = collections.OrderedDict()
features["token"] = token
features["layers"] = all_layers
all_features.append(features)
output_dict["features"] = all_features
outputs.append(output_dict)
return outputs[0]
def extract_v1(self, sentence):
example = [InputExample(unique_id=0, text_a=sentence, text_b=None)]
features = convert_examples_to_features(examples=example,
seq_length=_max_seq_length,
tokenizer=self._tokenizer)
input_fn = input_fn_builder(features=features,
seq_length=_max_seq_length)
outputs = []
for output in self._estimator.predict(input_fn):
outputs.append(output)
return outputs[0]
def extracts_v1(sentences, estimator, tokenizer, sen_len=15):
examples = []
for idx, sentence in enumerate(sentences):
examples.append(
InputExample(unique_id=idx, text_a=sentence, text_b=None))
features = convert_examples_to_features(
examples=examples, seq_length=sen_len,
tokenizer=tokenizer) #, get_cls = get_cls)
input_fn = input_fn_builder(features=features, seq_length=sen_len)
outputs = []
for output in estimator.predict(input_fn):
outputs.append(output)
return outputs
def _make_examples(self, texts):
"""Creates BERT examples and input_fn to iterate over them.
Args:
texts: List of strings. One example will be created per string.
Returns:
Dictionary mapping from unique example ID to example
"""
print('MAKING EXAMPLES')
examples = [
extract_features.InputExample(i, text, None)
for i, text in enumerate(texts)
]
features = extract_features.convert_examples_to_features(
examples, self._max_seq_len, self._tokenizer)
unique_id_to_feature = {}
for feature in features:
unique_id_to_feature[feature.unique_id] = feature
input_fn = extract_features.input_fn_builder(
features=features, seq_length=self._max_seq_len)
return unique_id_to_feature, input_fn