如何使用

from openmind import AutoTokenizer, AutoModelForSequenceClassification, is_torch_npu_available
import argparse
def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to model",
        default="ChongqingAscend/distilroberta-finetuned-financial-text-classification",
    )
    args = parser.parse_args()
    return args

args = parse_args()
model_path = args.model_name_or_path

if is_torch_npu_available():
    device = "npu:0"
else:
    device = "cpu"


tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path).to(device)

text = "I seem to have a cold."
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)

model.eval()
outputs = model(**inputs)
print(outputs)