基于英语语言的预训练模型，采用掩码语言建模（MLM）和下一句预测（NSP）目标。该模型在此论文中被提出，并首次在此仓库发布。 此模型区分大小写：它会区分“english”和“English”。该模型在MLM目标上达到0.58的准确率，在NSP目标上达到0.80的准确率。

免责声明：此模型卡片由gchhablani编写。 import argparse from transformers import FNetModel import torch import torch_npu from torch_npu.contrib import transfer_to_npu from openmind import pipeline, is_torch_npu_available, AutoTokenizer #from transformers import pipeline

torch.npu.set_compile_mode(jit_compile=False)

parser = argparse.ArgumentParser() parser.add_argument( "--model_name_or_path", type=str, help="Path to model", default=None, ) args = parser.parse_args()

if is_torch_npu_available(): device = "npu:0" else: device = "cpu" model_path = args.model_name_or_path tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, use_fast=False) model = FNetModel.from_pretrained(model_path) inputs = tokenizer("[|Human|]:三国演义的作者是谁？[|AI|]:", return_tensors="pt") pred = model(**inputs) print(pred)