BERT基础模型（ uncased ）

from openmind import pipeline, is_torch_npu_available
from openmind import AutoTokenizer, AutoModelForCausalLM
from openmind_hub import snapshot_download
import torch.nn.functional as F
from torch import Tensor
import openmind
import torch
import argparse
import time

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to model",
        default="jeffding/bert-base-uncased-openmind",
    )
    args = parser.parse_args()
    return args

def main():
    args = parse_args()
    model_path = args.model_name_or_path

    if is_torch_npu_available():
        device = "npu:0"
    else:
        device = "cpu"
    
    start_time = time.time()
    classifier = pipeline(task="text-classification", model=model_path, top_k=None, device=device)

    sentences = ["I am not having a great day"]

    model_outputs = classifier(sentences)
    print(model_outputs[0])
    
    end_time = time.time()
    print(f"硬件环境：{device},推理执行时间：{end_time - start_time}秒")
    
if __name__ == "__main__":
    main()

>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("Hello I'm a [MASK] model.")

[{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
  'score': 0.1073106899857521,
  'token': 4827,
  'token_str': 'fashion'},
 {'sequence': "[CLS] hello i'm a role model. [SEP]",
  'score': 0.08774490654468536,
  'token': 2535,
  'token_str': 'role'},
 {'sequence': "[CLS] hello i'm a new model. [SEP]",
  'score': 0.05338378623127937,
  'token': 2047,
  'token_str': 'new'},
 {'sequence': "[CLS] hello i'm a super model. [SEP]",
  'score': 0.04667217284440994,
  'token': 3565,
  'token_str': 'super'},
 {'sequence': "[CLS] hello i'm a fine model. [SEP]",
  'score': 0.027095865458250046,
  'token': 2986,
  'token_str': 'fine'}]

from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained("bert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)

from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertModel.from_pretrained("bert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)

>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("The man worked as a [MASK].")

[{'sequence': '[CLS] the man worked as a carpenter. [SEP]',
  'score': 0.09747550636529922,
  'token': 10533,
  'token_str': 'carpenter'},
 {'sequence': '[CLS] the man worked as a waiter. [SEP]',
  'score': 0.0523831807076931,
  'token': 15610,
  'token_str': 'waiter'},
 {'sequence': '[CLS] the man worked as a barber. [SEP]',
  'score': 0.04962705448269844,
  'token': 13362,
  'token_str': 'barber'},
 {'sequence': '[CLS] the man worked as a mechanic. [SEP]',
  'score': 0.03788609802722931,
  'token': 15893,
  'token_str': 'mechanic'},
 {'sequence': '[CLS] the man worked as a salesman. [SEP]',
  'score': 0.037680890411138535,
  'token': 18968,
  'token_str': 'salesman'}]

>>> unmasker("The woman worked as a [MASK].")

[{'sequence': '[CLS] the woman worked as a nurse. [SEP]',
  'score': 0.21981462836265564,
  'token': 6821,
  'token_str': 'nurse'},
 {'sequence': '[CLS] the woman worked as a waitress. [SEP]',
  'score': 0.1597415804862976,
  'token': 13877,
  'token_str': 'waitress'},
 {'sequence': '[CLS] the woman worked as a maid. [SEP]',
  'score': 0.1154729500412941,
  'token': 10850,
  'token_str': 'maid'},
 {'sequence': '[CLS] the woman worked as a prostitute. [SEP]',
  'score': 0.037968918681144714,
  'token': 19215,
  'token_str': 'prostitute'},
 {'sequence': '[CLS] the woman worked as a cook. [SEP]',
  'score': 0.03042375110089779,
  'token': 5660,
  'token_str': 'cook'}]

[CLS] Sentence A [SEP] Sentence B [SEP]

@article{DBLP:journals/corr/abs-1810-04805,
  author    = {Jacob Devlin and
               Ming{-}Wei Chang and
               Kenton Lee and
               Kristina Toutanova},
  title     = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
               Understanding},
  journal   = {CoRR},
  volume    = {abs/1810.04805},
  year      = {2018},
  url       = {http://arxiv.org/abs/1810.04805},
  archivePrefix = {arXiv},
  eprint    = {1810.04805},
  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}