小编you*_*ung的帖子

当我运行 demo.py 时

from transformers import AutoTokenizer, AutoModel\n    \ntokenizer = AutoTokenizer.from_pretrained("distilbert-base-multilingual-cased")\nmodel = AutoModel.from_pretrained("distilbert-base-multilingual-cased", return_dict=True)\n# print(model)\ndef count_parameters(model):\n    return sum(p.numel() for p in model.parameters() if p.requires_grad)\nprint(count_parameters(model))\ninputs = tokenizer("\xe5\x8f\xb2\xe5\xaf\x86\xe6\x96\xaf\xe5\x85\x88\xe7\x94\x9f\xe4\xb8\x8d\xe5\x9c\xa8\xef\xbc\x8c\xe4\xbb\x96\xe5\x8e\xbb\xe7\x9c\x8b\xe7\x94\xb5\xe5\xbd\xb1\xe4\xba\x86\xe3\x80\x82Mr Smith is not in. He ________ ________to the cinema", return_tensors="pt")\nprint(inputs)\noutputs = model(**inputs)\nprint(outputs)\n

代码显示

{'input_ids': tensor([[  101,  2759,  3417,  4332,  2431,  5600,  2080,  3031, 10064,  2196,\n      2724,  5765,  5614,  3756,  2146,  1882, 12916, 11673, 10124, 10472,\n     10106,   119, 10357,   168,   168,   168,   168,   168,   168,   168,\n       168,   168,   168,   168,   168,   168, …