| from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast | |
| def get_tokenlizer(text_encoder_type): | |
| if not isinstance(text_encoder_type, str): | |
| # print("text_encoder_type is not a str") | |
| if hasattr(text_encoder_type, "text_encoder_type"): | |
| text_encoder_type = text_encoder_type.text_encoder_type | |
| elif text_encoder_type.get("text_encoder_type", False): | |
| text_encoder_type = text_encoder_type.get("text_encoder_type") | |
| else: | |
| raise ValueError( | |
| "Unknown type of text_encoder_type: {}".format(type(text_encoder_type)) | |
| ) | |
| print("final text_encoder_type: {}".format(text_encoder_type)) | |
| tokenizer = AutoTokenizer.from_pretrained(text_encoder_type) | |
| return tokenizer | |
| def get_pretrained_language_model(text_encoder_type): | |
| if text_encoder_type == "bert-base-uncased": | |
| return BertModel.from_pretrained(text_encoder_type) | |
| if text_encoder_type == "roberta-base": | |
| return RobertaModel.from_pretrained(text_encoder_type) | |
| raise ValueError("Unknown text_encoder_type {}".format(text_encoder_type)) | |