from __future__ import absolute_import, division, print_function
import json
import os
import torch
import torch.nn.functional as F
from pytorch_transformers import (BertConfig, BertForTokenClassification,
BertTokenizer)
from collections import OrderedDict
import hydra
from hydra import utils
[docs]class BertNer(BertForTokenClassification):
[docs] 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')
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
[docs]class InferNer:
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 = "cuda" if torch.cuda.is_available() else "cpu"
self.model = self.model.to(self.device)
self.model.eval()
[docs] 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
[docs] def tokenize(self, text: str):
""" tokenize input"""
words = list(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
[docs] 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
[docs] def predict(self, text: str):
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_confidence = [values[label].item() for values,label in zip(logits[0],logits_label)]
logits = []
pos = 0
for index,mask in enumerate(valid_ids[0]):
if index == 0:
continue
if mask == 1:
logits.append((logits_label[index-pos],logits_confidence[index-pos]))
else:
pos += 1
logits.pop()
labels = [(self.label_map[label],confidence) for label,confidence in logits]
words = list(text)
assert len(labels) == len(words)
result = []
for word, (label, confidence) in zip(words, labels):
if label!='O':
result.append((word,label))
tmp = []
tag = OrderedDict()
tag['PER'] = []
tag['LOC'] = []
tag['ORG'] = []
for i, (word, label) in enumerate(result):
if label=='B-PER' or label=='B-LOC' or label=='B-ORG':
if i==0:
tmp.append(word)
else:
wordstype = result[i-1][1][2:]
tag[wordstype].append(''.join(tmp))
tmp.clear()
tmp.append(word)
else:
tmp.append(word)
if i==len(result)-1:
if label=='B-PER' or label=='B-LOC' or label=='B-ORG':
tmp.append(word)
wordstype = result[i][1][2:]
tag[wordstype].append(''.join(tmp))
return tag