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predict.py
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predict.py
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from modules.model import *
from transformers.tokenization_bert import BertTokenizer
from processor import NERProcessor, Example
from commons import NERdataset, FeatureExtractor
from torch.utils.data import DataLoader
from underthesea import sent_tokenize, word_tokenize
import torch
import torch.nn as nn
import logging
import argparse
class NER:
def __init__(self, pretrain_dir="pretrains/baseline/models",
feat_dir=None,
max_seq_length=256,
batch_size=4,
device=torch.device('cpu')):
self.tokenizer = BertTokenizer.from_pretrained("bert-base-multilingual-cased")
processor = NERProcessor(None, self.tokenizer)
self.fe = FeatureExtractor(dict_dir=feat_dir) if feat_dir is not None else None
self.label_list = processor.labels
self.max_seq_length = max_seq_length
self.batch_size = batch_size
self.device = device
num_labels = processor.get_num_labels()
_, self.model, self.feature = model_builder_from_pretrained("bert-base-multilingual-cased",
num_labels,
pretrain_dir,
feat_dir=feat_dir)
self.model.to(device)
def convert_sentences_to_features(self, sentences):
features = []
for sent_id, sentence in enumerate(sentences):
if self.fe is None:
words = " ".join(word_tokenize(sentence))
ex_words = words.split()
else:
ex_words, ex_feats = self.fe.extract_feature(sentence)
print(f"Input tokens: {ex_words}")
tokens = []
feats = {}
label_ids = []
token_masks = []
for i, word in enumerate(ex_words):
token = self.tokenizer.tokenize(word)
tokens.extend(token)
for m in range(len(token)):
if m == 0:
token_masks.append(1)
if self.fe is not None:
for feat_key, feat_value in ex_feats[i]:
feat_id = self.feature.feature_infos[feat_key]['label'].index(feat_value) + 1
if feat_key not in feats:
feats[feat_key] = [feat_id]
else:
feats[feat_key].append(feat_id)
else:
token_masks.append(0)
if self.fe is not None:
for feat_key, _ in ex_feats[i]:
feats[feat_key].append(0)
if len(tokens) >= self.max_seq_length - 1:
tokens = tokens[0:(self.max_seq_length - 2)]
token_masks = token_masks[0:(self.max_seq_length - 2)]
for k, v in feats.items():
feats[k] = v[0:(self.max_seq_length - 2)]
ntokens = []
# Add [CLS] token
ntokens.append("[CLS]")
token_masks.insert(0, 0)
if self.fe is not None:
for feat_key, feat_value in self.feature.special_token["[CLS]"]:
feat_id = self.feature.feature_infos[feat_key]['label'].index(feat_value) + 1
feats[feat_key].insert(0, feat_id)
ntokens.extend(tokens)
# Add [SEP] token
ntokens.append("[SEP]")
token_masks.append(0)
if self.fe is not None:
for feat_key, feat_value in self.feature.special_token["[CLS]"]:
feat_id = self.feature.feature_infos[feat_key]['label'].index(feat_value) + 1
feats[feat_key].insert(0, feat_id)
input_ids = self.tokenizer.convert_tokens_to_ids(ntokens)
attention_masks = [1] * len(input_ids)
label_masks = [1] * sum(token_masks)
segment_ids = [0] * self.max_seq_length
padding = [0] * (self.max_seq_length - len(input_ids))
input_ids.extend(padding)
attention_masks.extend(padding)
token_masks.extend(padding)
for k in feats.keys():
feats[k].extend(padding)
padding = [0] * (self.max_seq_length - len(label_masks))
label_masks.extend(padding)
assert len(input_ids) == self.max_seq_length
assert len(attention_masks) == self.max_seq_length
assert len(segment_ids) == self.max_seq_length
assert len(label_masks) == self.max_seq_length
assert len(token_masks) == self.max_seq_length
assert sum(token_masks) == sum(label_masks)
for k in feats.keys():
assert len(feats[k]) == self.max_seq_length
features.append(Example(eid=sent_id,
tokens=" ".join(ex_words),
token_ids=input_ids,
attention_masks=attention_masks,
segment_ids=segment_ids,
label_ids=label_ids,
label_masks=label_masks,
token_masks=token_masks,
feats=feats))
return features
def preprocess(self, text):
sentences = sent_tokenize(text)
features = self.convert_sentences_to_features(sentences)
data = NERdataset(features, self.device)
return DataLoader(data, batch_size=self.batch_size)
def predict(self, text):
entites = []
iterator = self.preprocess(text)
for step, batch in enumerate(iterator):
sents, token_ids, attention_masks, token_masks, segment_ids, label_ids, label_masks, feats = batch
logits = self.model(token_ids, attention_masks, token_masks, segment_ids, label_masks, feats)
logits = torch.argmax(nn.functional.softmax(logits, dim=-1), dim=-1)
pred = logits.detach().cpu().numpy()
entity = None
words = []
for sent in sents:
words.extend(sent.split())
for p, w in list(zip(pred, words)):
label = self.label_list[p-1]
if not label == 'O':
prefix, label = label.split('-')
if entity is None:
entity = (w, label)
else:
if entity[-1] == label:
if prefix == 'I':
entity = (entity[0] + f' {w}', label)
else:
entites.append(entity)
entity = (w, label)
else:
entites.append(entity)
entity = (w, label)
elif entity is not None:
entites.append(entity)
entity = None
else:
entity = None
return entites
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--pretrain_dir", default=None, type=str, required=True)
parser.add_argument("--feat_dir", default=None, type=str)
parser.add_argument("--max_seq_length", default=128, type=int)
parser.add_argument("--batch_size", default=8, type=int)
parser.add_argument("--cuda", action="store_true")
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
ner = NER(args.pretrain_dir, args.feat_dir, args.max_seq_length, args.batch_size, device)
while True:
input_text = input("Enter text: ")
if len(input_text.strip()) == 0:
print("Input NULL, auto use text sample!!!!")
input_text = """Ông Nguyễn Đức Vinh - giám đốc Sở Nông nghiệp và phát triển nông thôn Hà Giang - nhận
định mưa lớn cục bộ trong thời gian ngắn là nguyên nhân chính dẫn đến tình trạng ngập lụt chưa từng có ở
TP Hà Giang. Mặt khác, theo ông Vinh, TP Hà Giang có địa hình lòng chảo, bao xung quanh là núi cũng khiến
lượng nước đổ dồn về trung tâm rất lớn."""
print(ner.predict(input_text))