def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, albert_config_file, pytorch_dump_path): # Initialise PyTorch model config = AlbertConfig.from_json_file(albert_config_file) print("Building PyTorch model from configuration: {}".format(str(config))) model = AlbertForMaskedLM(config) load_tf_weights_in_albert(model, config, tf_checkpoint_path) print("Save PyTorch model to {}".format(pytorch_dump_path)) torch.save(model.state_dict(), pytorch_dump_path)
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, albert_config_file, pytorch_dump_path): # Initialise PyTorch model config = AlbertConfig.from_json_file(albert_config_file) print(f"Building PyTorch model from configuration: {config}") model = AlbertForPreTraining(config) # Load weights from tf checkpoint load_tf_weights_in_albert(model, config, tf_checkpoint_path) # Save pytorch-model print(f"Save PyTorch model to {pytorch_dump_path}") torch.save(model.state_dict(), pytorch_dump_path)
def albert_convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, albert_config_file, pytorch_dump_path): from transformers import AlbertConfig, AlbertForMaskedLM, load_tf_weights_in_albert # Initialise PyTorch model config = AlbertConfig.from_json_file(albert_config_file) print("Building PyTorch model from configuration: {}".format(str(config))) model = AlbertForMaskedLM(config) # Load weights from tf checkpoint load_tf_weights_in_albert(model, config, tf_checkpoint_path) # Save pytorch-model print("Save PyTorch model to {}".format(pytorch_dump_path)) torch.save(model.state_dict(), pytorch_dump_path)
def main(args): with open(args.config) as fp: data = json.loads(fp.read()) config = AlbertConfig(**data) model = AlbertForMaskedLM(config) model: AlbertForMaskedLM = load_tf_weights_in_albert(model, config, args.checkpoint) model.save_pretrained(args.output)
def main(args): with open(args.config) as fp: data = json.loads(fp.read()) config = AlbertConfig(**data) model = AlbertForMaskedLM(config) model: AlbertForMaskedLM = load_tf_weights_in_albert( model, config, args.checkpoint) model.save_pretrained(args.output) tokenizer = AlbertTokenizer.from_pretrained(args.spiece, keep_accents=True) tokenizer.save_pretrained(args.output)