Model/NER/VLSP2021/Load_model.py

from transformers import RobertaConfig, AutoConfig
from transformers import AutoTokenizer, AutoModelForTokenClassification
from Model.NER.VLSP2021.Ner_CRF import PhoBertCrf,PhoBertSoftmax,PhoBertLstmCrf
from Model.NER.VLSP2021.Predict_Ner import ViTagger
import torch
from spacy import displacy
import re
device = 'cuda' if torch.cuda.is_available() else 'cpu'
MODEL_MAPPING = {
    'vinai/phobert-base': {
        'softmax': PhoBertSoftmax,
        'crf': PhoBertCrf,
        'lstm_crf': PhoBertLstmCrf
    },
}
if device == 'cpu':
        checkpoint_data = torch.load('/Model/NER/VLSP2021/best_model.pt', map_location='cpu')
else:
        checkpoint_data = torch.load('/Model/NER/VLSP2021/best_model.pt')

configs = checkpoint_data['args']
print(configs.model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(configs.model_name_or_path)
model_clss = MODEL_MAPPING[configs.model_name_or_path][configs.model_arch]
config = AutoConfig.from_pretrained(configs.model_name_or_path,
                                        num_labels=len(checkpoint_data['classes']),
                                        finetuning_task=configs.task)
model = model_clss(config=config)
model.resize_token_embeddings(len(tokenizer))
model.to(device)
model.load_state_dict(checkpoint_data['model'],strict=False)
print(model)