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| import torch | |
| from transformers import (AutoTokenizer, BertForTokenClassification, | |
| get_linear_schedule_with_warmup) | |
| # Assuming the JSON data is stored in a file named 'data.json' | |
| DEFAULT_TEXT_ANNOTATION_FILE = "Datasets/Query/datasets_text.json" | |
| # Pretrained model | |
| pretrain_model_name = "vinai/phobert-base-v2" | |
| tokenizer = AutoTokenizer.from_pretrained(pretrain_model_name) | |
| # Hyperparameters for training | |
| batch_size = 64 # Number of samples per batch | |
| epochs = 50 # Number of training epochs | |
| device = "cuda" if torch.cuda.is_available() else "cpu" # Check if GPU is available | |
| lr = 5e-5 | |
| eps = 1e-8 | |
| weight_decay= 1e-5 | |
| # Paths for saving the trained model and test response tags | |
| model_saved_path = "Model_API\Saved_Model\key_ner_new_data_method" | |
| model_load_path = "Model_API/Saved_Model/key_ner_new_data_method" | |
| save_respone_tags_path = "Datasets/Query/answer_test.json" | |
| onnx_path = "Model_API/Saved_Model/key_ner.onnx" | |