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Use pytorch-transformers from hugging face to get bert embeddings in pytorch
import torch
import logging
from pytorch_transformers import BertModel, BertTokenizer
from pytorch_transformers import *
from typing import List
CLS_TOKEN = "[CLS]"
SEP_TOKEN = "[SEP]"
logger = logging.getLogger(__name__)
class InputExample(object):
"""A single training/test example for simple sequence classification."""
def __init__(self, guid, text_a, text_b=None, label=None):
"""Constructs a InputExample.
Args:
guid: Unique id for the example.
text_a: string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
text_b: (Optional) string. The untokenized text of the second sequence.
Only must be specified for sequence pair tasks.
label: (Optional) string. The label of the example. This should be
specified for train and dev examples, but not for test examples.
"""
self.guid = guid
self.text_a = text_a
self.text_b = text_b
self.label = label
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self, input_ids, input_mask, segment_ids):
self.input_ids = input_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
def convert_examples_to_features(examples, tokenizer, max_seq_length,
cls_token_at_end=False, pad_on_left=False,
cls_token='[CLS]', sep_token='[SEP]', pad_token=0,
sequence_a_segment_id=0, sequence_b_segment_id=1,
cls_token_segment_id=1, pad_token_segment_id=0,
mask_padding_with_zero=True):
""" Loads a data file into a list of `InputBatch`s
`cls_token_at_end` define the location of the CLS token:
- False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
- True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
`cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)
"""
features = []
for (ex_index, example) in enumerate(examples):
if ex_index % 10000 == 0:
logger.info("Writing example %d of %d" % (ex_index, len(examples)))
tokens_a = tokenizer.tokenize(example.text_a)
tokens_b = None
if example.text_b:
tokens_b = tokenizer.tokenize(example.text_b)
# Modifies `tokens_a` and `tokens_b` in place so that the total
# length is less than the specified length.
# Account for [CLS], [SEP], [SEP] with "- 3"
_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
else:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[:(max_seq_length - 2)]
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambiguously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens = tokens_a + [sep_token]
segment_ids = [sequence_a_segment_id] * len(tokens)
if tokens_b:
tokens += tokens_b + [sep_token]
segment_ids += [sequence_b_segment_id] * (len(tokens_b) + 1)
if cls_token_at_end:
tokens = tokens + [cls_token]
segment_ids = segment_ids + [cls_token_segment_id]
else:
tokens = [cls_token] + tokens
segment_ids = [cls_token_segment_id] + segment_ids
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
# Zero-pad up to the sequence length.
padding_length = max_seq_length - len(input_ids)
if pad_on_left:
input_ids = ([pad_token] * padding_length) + input_ids
input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids
else:
input_ids = input_ids + ([pad_token] * padding_length)
input_mask = input_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
segment_ids = segment_ids + ([pad_token_segment_id] * padding_length)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
if ex_index < 5:
logger.info("*** Example ***")
logger.info("guid: %s" % (example.guid))
logger.info("tokens: %s" % " ".join(
[str(x) for x in tokens]))
logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
logger.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
features.append(
InputFeatures(input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids))
return features
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
"""Truncates a sequence pair in place to the maximum length."""
# This is a simple heuristic which will always truncate the longer sequence
# one token at a time. This makes more sense than truncating an equal percent
# of tokens from each, since if one sequence is very short then each token
# that's truncated likely contains more information than a longer sequence.
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_length:
break
if len(tokens_a) > len(tokens_b):
tokens_a.pop()
else:
tokens_b.pop()
def select_field(features, field):
"""As the output is dic, return relevant field"""
return [[choice[field] for choice in feature.choices_features] for feature in features]
def create_examples(_list, set_type="train"):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(_list):
guid = "%s-%s" % (set_type, i)
text_a = line
# text_b = line[1]
examples.append(
InputExample(guid=guid, text_a=text_a))
return examples
class BertEmbedder:
def __init__(self,
pretrained_weights='bert-base-uncased',
tokenizer_class=BertTokenizer,
model_class=BertModel,
max_seq_len=20):
super().__init__()
self.pretrained_weights = pretrained_weights
self.tokenizer_class = tokenizer_class
self.model_class = model_class
self.tokenizer = self.tokenizer_class.from_pretrained(pretrained_weights)
self.model = self.model_class.from_pretrained(pretrained_weights)
self.max_seq_len = max_seq_len
# tokenizer = BertTokenizer.from_pretrained(pretrained_weights)
# model = BertModel.from_pretrained(pretrained_weights)
def get_bert_embeddings(self,
raw_text: List[str]) -> torch.tensor:
examples = create_examples(raw_text)
features = convert_examples_to_features(
examples, self.tokenizer, self.max_seq_len, True)
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
last_hidden_states = self.model(all_input_ids)[0] # Models outputs are now tuples
print(last_hidden_states.size())
return last_hidden_states
if __name__=="__main__":
embedder = BertEmbedder()
raw_text = ["[CLS] This is first element [SEP] continuing statement",
"[CLS] second element of the list."]
bert_embedding = embedder.get_bert_embeddings(raw_text)
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