175 lines
6.3 KiB
Python
175 lines
6.3 KiB
Python
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"""BERT NER Inference."""
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from __future__ import absolute_import, division, print_function
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import json
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import os
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import torch
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import torch.nn.functional as F
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from nltk import word_tokenize
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from pytorch_transformers import (BertConfig, BertForTokenClassification,
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BertTokenizer)
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from collections import OrderedDict
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import argparse
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import nltk
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nltk.data.path.insert(0,'./data/nltk_data')
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class BertNer(BertForTokenClassification):
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def forward(self, input_ids, token_type_ids=None, attention_mask=None, valid_ids=None):
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sequence_output = self.bert(input_ids, token_type_ids, attention_mask, head_mask=None)[0]
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batch_size,max_len,feat_dim = sequence_output.shape
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valid_output = torch.zeros(batch_size,max_len,feat_dim,dtype=torch.float32,device='cuda' if torch.cuda.is_available() else 'cpu')
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for i in range(batch_size):
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jj = -1
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for j in range(max_len):
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if valid_ids[i][j].item() == 1:
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jj += 1
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valid_output[i][jj] = sequence_output[i][j]
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sequence_output = self.dropout(valid_output)
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logits = self.classifier(sequence_output)
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return logits
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class Ner:
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def __init__(self,model_dir: str):
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self.model , self.tokenizer, self.model_config = self.load_model(model_dir)
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self.label_map = self.model_config["label_map"]
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self.max_seq_length = self.model_config["max_seq_length"]
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self.label_map = {int(k):v for k,v in self.label_map.items()}
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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self.model = self.model.to(self.device)
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self.model.eval()
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def load_model(self, model_dir: str, model_config: str = "model_config.json"):
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model_config = os.path.join(model_dir,model_config)
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model_config = json.load(open(model_config))
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model = BertNer.from_pretrained(model_dir)
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tokenizer = BertTokenizer.from_pretrained(model_dir, do_lower_case=model_config["do_lower"])
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return model, tokenizer, model_config
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def tokenize(self, text: str):
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""" tokenize input"""
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words = word_tokenize(text)
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tokens = []
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valid_positions = []
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for i,word in enumerate(words):
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token = self.tokenizer.tokenize(word)
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tokens.extend(token)
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for i in range(len(token)):
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if i == 0:
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valid_positions.append(1)
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else:
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valid_positions.append(0)
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return tokens, valid_positions
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def preprocess(self, text: str):
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""" preprocess """
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tokens, valid_positions = self.tokenize(text)
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## insert "[CLS]"
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tokens.insert(0,"[CLS]")
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valid_positions.insert(0,1)
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## insert "[SEP]"
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tokens.append("[SEP]")
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valid_positions.append(1)
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segment_ids = []
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for i in range(len(tokens)):
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segment_ids.append(0)
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input_ids = self.tokenizer.convert_tokens_to_ids(tokens)
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input_mask = [1] * len(input_ids)
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while len(input_ids) < self.max_seq_length:
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input_ids.append(0)
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input_mask.append(0)
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segment_ids.append(0)
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valid_positions.append(0)
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return input_ids,input_mask,segment_ids,valid_positions
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def predict(self, text: str):
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input_ids,input_mask,segment_ids,valid_ids = self.preprocess(text)
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input_ids = torch.tensor([input_ids],dtype=torch.long,device=self.device)
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input_mask = torch.tensor([input_mask],dtype=torch.long,device=self.device)
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segment_ids = torch.tensor([segment_ids],dtype=torch.long,device=self.device)
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valid_ids = torch.tensor([valid_ids],dtype=torch.long,device=self.device)
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with torch.no_grad():
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logits = self.model(input_ids, segment_ids, input_mask,valid_ids)
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logits = F.softmax(logits,dim=2)
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logits_label = torch.argmax(logits,dim=2)
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logits_label = logits_label.detach().cpu().numpy().tolist()[0]
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logits_confidence = [values[label].item() for values,label in zip(logits[0],logits_label)]
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logits = []
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pos = 0
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for index,mask in enumerate(valid_ids[0]):
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if index == 0:
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continue
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if mask == 1:
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logits.append((logits_label[index-pos],logits_confidence[index-pos]))
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else:
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pos += 1
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logits.pop()
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labels = [(self.label_map[label],confidence) for label,confidence in logits]
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words = word_tokenize(text)
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assert len(labels) == len(words)
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result = []
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for word, (label, confidence) in zip(words, labels):
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if label!='O':
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result.append((word,label))
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tmp = []
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tag = OrderedDict()
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tag['PER'] = []
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tag['LOC'] = []
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tag['ORG'] = []
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tag['MISC'] = []
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for i, (word, label) in enumerate(result):
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if label=='B-PER' or label=='B-LOC' or label=='B-ORG' or label=='B-MISC':
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if i==0:
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tmp.append(word)
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else:
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wordstype = result[i-1][1][2:]
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tag[wordstype].append(' '.join(tmp))
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tmp.clear()
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tmp.append(word)
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elif i==len(result)-1:
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tmp.append(word)
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wordstype = result[i][1][2:]
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tag[wordstype].append(' '.join(tmp))
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else:
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tmp.append(word)
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return tag
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if __name__ == "__main__":
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model = Ner("out_ner/")
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parser = argparse.ArgumentParser()
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parser.add_argument("--text",
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default="Irene, a master student in Zhejiang University, Hangzhou, is traveling in Warsaw for Chopin Music Festival.",
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type=str,
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help="The text to be NERed")
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text = parser.parse_args().text
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print("The text to be NERed:")
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print(text)
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print('Results of NER:')
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result = model.predict(text)
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for k,v in result.items():
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if v:
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print(v,end=': ')
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if k=='PER':
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print('Person')
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elif k=='LOC':
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print('Location')
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elif k=='ORG':
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print('Organization')
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elif k=='MISC':
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print('Miscellaneous')
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