bert-base-chinese另外的加载方法.txt

LIjin_10062024-03-25 17:09

import os

os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'

import torch

from transformers import pipeline

pipe = pipeline("fill-mask", model="google-bert/bert-base-chinese")

from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-chinese")

model = AutoModelForMaskedLM.from_pretrained("google-bert/bert-base-chinese")

input_text='bert-base-chinese该怎么用?'

inx_text=tokenizer.encode(input_text)

inv_text=tokenizer.convert_ids_to_tokens(inx_text)

with torch.no_grad():

output=model(torch.tensor([inx_text]))

print(type(output))

last_hidden=output['logits']

print(last_hidden.shape,last_hidden[0])# (1,9,21128)(batch_size,seq_len,d_model)

from transformers import AutoModelForQuestionAnswering

aq=AutoModelForQuestionAnswering.from_pretrained("google-bert/bert-base-chinese")

定义问题和上下文

question = "你好，请问今天天气怎么样？"

context = "今天是晴天，气温适中，非常适合户外活动。"

使用分词器对问题和上下文进行编码

inputs = tokenizer(question, context, return_tensors='pt', \

padding=True, truncation=True)

input_ids = inputs['input_ids']

attention_mask = inputs['attention_mask']

with torch.no_grad():

aq_outputs =aq(input_ids, attention_mask=attention_mask)

start_inxes=aq_outputs['start_logits'][0]

end_inxes=aq_outputs['end_logits'][0]

print(len(start_inxes),len(end_inxes))

start=torch.argmax(start_inxes)

end=torch.argmax(end_inxes)

(question+context)[start:end]