File size: 6,054 Bytes
f0d2bd1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 |
"""BERT NER Inference."""
import json
import os
import torch
import torch.nn.functional as F
from nltk import word_tokenize
from pytorch_transformers import (BertForTokenClassification, BertTokenizer)
class BertNer(BertForTokenClassification):
def forward(self, input_ids, token_type_ids=None, attention_mask=None, valid_ids=None):
sequence_output = self.bert(input_ids, token_type_ids, attention_mask, head_mask=None)[0]
batch_size,max_len,feat_dim = sequence_output.shape
# valid_output = torch.zeros(batch_size,max_len,feat_dim,dtype=torch.float32,device='cuda' if torch.cuda.is_available() else 'cpu')
valid_output = torch.zeros(batch_size,max_len,feat_dim,dtype=torch.float32,device='cpu')
for i in range(batch_size):
jj = -1
for j in range(max_len):
if valid_ids[i][j].item() == 1:
jj += 1
valid_output[i][jj] = sequence_output[i][j]
sequence_output = self.dropout(valid_output)
logits = self.classifier(sequence_output)
return logits
class BIOBERT_Ner:
def __init__(self,model_dir: str):
self.model , self.tokenizer, self.model_config = self.load_model(model_dir)
self.label_map = self.model_config["label_map"]
self.max_seq_length = self.model_config["max_seq_length"]
self.label_map = {int(k):v for k,v in self.label_map.items()}
self.device = "cpu"
# self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.model = self.model.to(self.device)
self.model.eval()
def load_model(self, model_dir: str, model_config: str = "model_config.json"):
model_config = os.path.join(model_dir,model_config)
model_config = json.load(open(model_config))
model = BertNer.from_pretrained(model_dir)
tokenizer = BertTokenizer.from_pretrained(model_dir, do_lower_case=model_config["do_lower"])
return model, tokenizer, model_config
def tokenize(self, text: str):
""" tokenize input"""
words = word_tokenize(text)
tokens = []
valid_positions = []
for i,word in enumerate(words):
token = self.tokenizer.tokenize(word)
tokens.extend(token)
for i in range(len(token)):
if i == 0:
valid_positions.append(1)
else:
valid_positions.append(0)
return tokens, valid_positions
def preprocess(self, text: str):
""" preprocess """
tokens, valid_positions = self.tokenize(text)
## insert "[CLS]"
tokens.insert(0,"[CLS]")
valid_positions.insert(0,1)
## insert "[SEP]"
tokens.append("[SEP]")
valid_positions.append(1)
segment_ids = []
for i in range(len(tokens)):
segment_ids.append(0)
input_ids = self.tokenizer.convert_tokens_to_ids(tokens)
input_mask = [1] * len(input_ids)
while len(input_ids) < self.max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
valid_positions.append(0)
return input_ids,input_mask,segment_ids,valid_positions
def predict_entity(self, B_lab, I_lab, words, labels, entity_list):
temp=[]
entity=[]
for word, label, B_l, I_l in zip(words, labels, B_lab, I_lab):
if ((label==B_l) or (label==I_l)) and label!='O':
if label==B_l:
entity.append(temp)
temp=[]
temp.append(label)
temp.append(word)
entity.append(temp)
entity_name_label = []
for entity_name in entity[1:]:
for ent_key, ent_value in entity_list.items():
if (ent_key==entity_name[0]):
entity_name_label.append([' '.join(entity_name[1:]), ent_value])
return entity_name_label
def predict(self, text: str):
print("text:", text)
input_ids,input_mask,segment_ids,valid_ids = self.preprocess(text)
input_ids = torch.tensor([input_ids],dtype=torch.long,device=self.device)
input_mask = torch.tensor([input_mask],dtype=torch.long,device=self.device)
segment_ids = torch.tensor([segment_ids],dtype=torch.long,device=self.device)
valid_ids = torch.tensor([valid_ids],dtype=torch.long,device=self.device)
with torch.no_grad():
logits = self.model(input_ids, segment_ids, input_mask,valid_ids)
logits = F.softmax(logits,dim=2)
logits_label = torch.argmax(logits,dim=2)
logits_label = logits_label.detach().cpu().numpy().tolist()[0]
logits = []
pos = 0
for index,mask in enumerate(valid_ids[0]):
if index == 0:
continue
if mask == 1:
logits.append((logits_label[index-pos]))
else:
pos += 1
logits.pop()
labels = [(self.label_map[label]) for label in logits]
words = word_tokenize(text)
entity_list = {'B-ANATOMY':'Anatomy', 'B-GENE':'Gene', 'B-CHEMICAL':'Chemical', 'B-DISEASE':'Disease', 'B-PROTEIN':'Protein', 'B-ORGANISM':'Organism', 'B-CANCER':'Cancer', 'B-ORGAN':'Organ', 'B-CELL':'Cell', 'B-TISSUE':'Tissue', 'B-PATHOLOGY_TERM':'Pathlogy', 'B-COMPLEX':'Complex', 'B-TAXON':'Taxon'}
B_labels=[]
I_labels=[]
for label in labels:
if (label[:1]=='B'):
B_labels.append(label)
I_labels.append('O')
elif (label[:1]=='I'):
I_labels.append(label)
B_labels.append('O')
else:
B_labels.append('O')
I_labels.append('O')
assert len(labels) == len(words) == len(I_labels) == len(B_labels)
output = self.predict_entity(B_labels, I_labels, words, labels, entity_list)
return output
|