def _base(self, strings, labels):
strings_left, strings_right, mapping = [], [], defaultdict(list)
index = 0
for no, string in enumerate(strings):
for label in labels:
strings_left.append(string)
strings_right.append(f'teks ini adalah mengenai {label}')
mapping[no].append(index)
index += 1
input_ids, input_masks, segment_ids, _ = xlnet_tokenization_siamese(
self._tokenizer, strings_left, strings_right)
r = self._execute(
inputs=[input_ids, segment_ids, input_masks],
input_labels=['Placeholder', 'Placeholder_1', 'Placeholder_2'],
output_labels=['logits'],
)
output = softmax(r['logits'], axis=-1)
results = []
for k, v in mapping.items():
result = {}
for no, index in enumerate(v):
result[labels[no]] = output[index, 1]
results.append(result)
return results
def _base(self, strings, labels):
strings_left, strings_right, mapping = [], [], defaultdict(list)
index = 0
for no, string in enumerate(strings):
for label in labels:
strings_left.append(string)
strings_right.append(f'teks ini adalah mengenai {label}')
mapping[no].append(index)
index += 1
input_ids, input_masks, segment_ids, _ = xlnet_tokenization_siamese(
self._tokenizer, strings_left, strings_right)
output = self._sess.run(
self._softmax,
feed_dict={
self._X: input_ids,
self._segment_ids: segment_ids,
self._input_masks: input_masks,
},
)
results = []
for k, v in mapping.items():
result = {}
for no, index in enumerate(v):
result[labels[no]] = output[index, 1]
results.append(result)
return results
def vectorize(self,
strings: List[str],
labels: List[str],
method: str = 'first'):
"""
vectorize a string.
Parameters
----------
strings: List[str]
labels : List[str]
method : str, optional (default='first')
Vectorization layer supported. Allowed values:
* ``'last'`` - vector from last sequence.
* ``'first'`` - vector from first sequence.
* ``'mean'`` - average vectors from all sequences.
* ``'word'`` - average vectors based on tokens.
Returns
-------
result: np.array
"""
strings_left, strings_right, combined = [], [], []
for no, string in enumerate(strings):
for label in labels:
strings_left.append(string)
strings_right.append(f'teks ini adalah mengenai {label}')
combined.append((string, label))
input_ids, input_masks, segment_ids, s_tokens = xlnet_tokenization_siamese(
self._tokenizer, strings_left, strings_right)
r = self._execute(
inputs=[input_ids, segment_ids, input_masks],
input_labels=['Placeholder', 'Placeholder_1', 'Placeholder_2'],
output_labels=['vectorizer'],
)
v = r['vectorizer']
v = np.transpose(v, [1, 0, 2])
if method == 'first':
v = v[:, 0]
elif method == 'last':
v = v[:, -1]
elif method == 'mean':
v = np.mean(v, axis=1)
else:
v = [
merge_sentencepiece_tokens(
list(zip(s_tokens[i], v[i][:len(s_tokens[i])])),
weighted=False,
vectorize=True,
model='xlnet',
) for i in range(len(v))
]
return combined, v
def _base(self, strings_left, strings_right):
input_ids, input_masks, segment_ids, _ = xlnet_tokenization_siamese(
self._tokenizer, strings_left, strings_right)
r = self._execute(
inputs=[input_ids, segment_ids, input_masks],
input_labels=['Placeholder', 'Placeholder_1', 'Placeholder_2'],
output_labels=['logits'],
)
return softmax(r['logits'], axis=-1)
def _base(self, strings_left, strings_right):
input_ids, input_masks, segment_ids, _ = xlnet_tokenization_siamese(
self._tokenizer, strings_left, strings_right)
return self._sess.run(
self._softmax,
feed_dict={
self._X: input_ids,
self._segment_ids: segment_ids,
self._input_masks: input_masks,
},
)
