forked from shibing624/python-tutorial
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy path7ner.py
More file actions
40 lines (37 loc) · 1.65 KB
/
7ner.py
File metadata and controls
40 lines (37 loc) · 1.65 KB
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
# -*- coding: utf-8 -*-
"""
@author:XuMing([email protected])
@description:
"""
from transformers import pipeline, TokenClassificationPipeline
import os
from transformers import AutoModelForTokenClassification, AutoTokenizer
import torch
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
model_dir = os.path.expanduser('/Users/xuming06/Documents/Data/chinese-xlnet-base/')
print(model_dir)
nlp = pipeline("ner",
model=model_dir, tokenizer=model_dir)
sequence = "王宏伟来自北京，是个警察，喜欢去王府井游玩儿。"
print(nlp(sequence))
# custom
# model = AutoModelForTokenClassification.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
# tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
model = AutoModelForTokenClassification.from_pretrained(model_dir)
tokenizer = AutoTokenizer.from_pretrained(model_dir)
label_list = [
"O", # Outside of a named entity
"B-PER", # Beginning of a person's name right after another person's name
"I-PER", # Person's name
"B-ORG", # Beginning of an organisation right after another organisation
"I-ORG", # Organisation
"B-LOC", # Beginning of a location right after another location
"I-LOC" # Location
]
sequence = "王宏伟来自北京，是个警察，喜欢去王府井游玩儿。"
# Bit of a hack to get the tokens with the special tokens
tokens = tokenizer.tokenize(tokenizer.decode(tokenizer.encode(sequence)))
inputs = tokenizer.encode(sequence, return_tensors="pt")
outputs = model(inputs).logits
predictions = torch.argmax(outputs, dim=2)
print([(token, label_list[prediction]) for token, prediction in zip(tokens, predictions[0].numpy())])