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我正在关注这篇文章以查找文本相似性。我的代码是这样的：

from sentence_transformers import SentenceTransformer
from tqdm import tqdm
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
import pandas as pd

documents = [
         "Vodafone Wins ₹ 20,000 Crore Tax Arbitration Case Against Government",
         "Voda Idea shares jump nearly 15% as Vodafone wins retro tax case in Hague",
         "Gold prices today fall for 4th time in 5 days, down ₹6500 from last month high",
         "Silver futures slip 0.36% to Rs 59,415 per kg, down over 12% this week",
         "Amazon unveils drone that films inside your home. What could go wrong?",
         "IPHONE 12 MINI PERFORMANCE MAY DISAPPOINT DUE TO THE APPLE B14 CHIP",
         "Delhi Capitals vs Chennai Super Kings: Prithvi Shaw shines as DC beat CSK to post second consecutive win in IPL",
         "French Open 2020: Rafael Nadal handed tough draw in bid for record-equaling 20th Grand Slam"
]

model = SentenceTransformer('sentence-transformers/bert-base-nli-mean-tokens')

运行上述代码时出现错误：

满的：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~\anaconda3\envs\py3_nlp\lib\tarfile.py in nti(s)
    188             s = nts(s, "ascii", "strict")
--> 189             n = int(s.strip() or "0", 8)
    190         except ValueError:

ValueError: invalid literal for int() with base 8: 'ld_tenso'

During handling of the above exception, another exception occurred:

InvalidHeaderError                        Traceback (most recent call last)
~\anaconda3\envs\py3_nlp\lib\tarfile.py in next(self)
   2298             try:
 -> 2299                 tarinfo = self.tarinfo.fromtarfile(self)
   2300             except EOFHeaderError as e:

 ~\anaconda3\envs\py3_nlp\lib\tarfile.py in fromtarfile(cls, tarfile)
   1092         buf = tarfile.fileobj.read(BLOCKSIZE)
 -> 1093         obj = cls.frombuf(buf, tarfile.encoding, tarfile.errors)
   1094         obj.offset = tarfile.fileobj.tell() - BLOCKSIZE

 ~\anaconda3\envs\py3_nlp\lib\tarfile.py in frombuf(cls, buf, encoding, errors)
    1034 
 -> 1035         chksum = nti(buf[148:156])
    1036         if chksum not in calc_chksums(buf):

  ~\anaconda3\envs\py3_nlp\lib\tarfile.py in nti(s)
    190         except ValueError:
--> 191             raise InvalidHeaderError("invalid header")
    192     return n

 InvalidHeaderError: invalid header

 During handling of the above exception, another exception occurred:

 ReadError                                 Traceback (most recent call last)
 ~\anaconda3\envs\py3_nlp\lib\site-packages\torch\serialization.py in _load(f, map_location, 
 pickle_module, **pickle_load_args)
     594         try:
 --> 595             return legacy_load(f)
     596         except tarfile.TarError:

 ~\anaconda3\envs\py3_nlp\lib\site-packages\torch\serialization.py in legacy_load(f)
    505 
--> 506         with closing(tarfile.open(fileobj=f, mode='r:', format=tarfile.PAX_FORMAT)) as 
tar, \
    507                 mkdtemp() as tmpdir:

 ~\anaconda3\envs\py3_nlp\lib\tarfile.py in open(cls, name, mode, fileobj, bufsize, **kwargs)
    1590                 raise CompressionError("unknown compression type %r" % comptype)
 -> 1591             return func(name, filemode, fileobj, **kwargs)
    1592 

~\anaconda3\envs\py3_nlp\lib\tarfile.py in taropen(cls, name, mode, fileobj, **kwargs)
   1620             raise ValueError("mode must be 'r', 'a', 'w' or 'x'")
-> 1621         return cls(name, mode, fileobj, **kwargs)
   1622 

 ~\anaconda3\envs\py3_nlp\lib\tarfile.py in __init__(self, name, mode, fileobj, format, tarinfo, dereference, ignore_zeros, encoding, errors, pax_headers, debug, errorlevel, copybufsize)
   1483                 self.firstmember = None
-> 1484                 self.firstmember = self.next()
   1485 

~\anaconda3\envs\py3_nlp\lib\tarfile.py in next(self)
   2310                 elif self.offset == 0:
 -> 2311                     raise ReadError(str(e))
   2312             except EmptyHeaderError:

 ReadError: invalid header

 During handling of the above exception, another exception occurred:

 RuntimeError                              Traceback (most recent call last)
 ~\anaconda3\envs\py3_nlp\lib\site-packages\transformers\modeling_utils.py in from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
    1210                 try:
 -> 1211                     state_dict = torch.load(resolved_archive_file, map_location="cpu")
    1212                 except Exception:

 ~\anaconda3\envs\py3_nlp\lib\site-packages\torch\serialization.py in load(f, map_location, pickle_module, **pickle_load_args)
     425             pickle_load_args['encoding'] = 'utf-8'
 --> 426         return _load(f, map_location, pickle_module, **pickle_load_args)
     427     finally:

 ~\anaconda3\envs\py3_nlp\lib\site-packages\torch\serialization.py in _load(f, map_location, pickle_module, **pickle_load_args)
    598                 # .zip is used for torch.jit.save and will throw an un-pickling error here
  --> 599                 raise RuntimeError("{} is a zip archive (did you mean to use torch.jit.load()?)".format(f.name))
     600             # if not a tarfile, reset file offset and proceed

 RuntimeError: C:\Users\user1/.cache\torch\sentence_transformers\sentence-transformers_bert-base-nli-mean-tokens\pytorch_model.bin is a zip archive (did you mean to use torch.jit.load()?)

 During handling of the above exception, another exception occurred:

 OSError                                   Traceback (most recent call last)
 <ipython-input-3-bba56aac60aa> in <module>
 ----> 1 model = SentenceTransformer('sentence-transformers/bert-base-nli-mean-tokens')

 ~\anaconda3\envs\py3_nlp\lib\site-packages\sentence_transformers\SentenceTransformer.py in __init__(self, model_name_or_path, modules, device, cache_folder)
     88 
     89             if os.path.exists(os.path.join(model_path, 'modules.json')):    #Load as SentenceTransformer model
---> 90                 modules = self._load_sbert_model(model_path)
     91             else:   #Load with AutoModel
     92                 modules = self._load_auto_model(model_path)

 ~\anaconda3\envs\py3_nlp\lib\site-packages\sentence_transformers\SentenceTransformer.py in _load_sbert_model(self, model_path)
     820         for module_config in modules_config:
     821             module_class = import_from_string(module_config['type'])
 --> 822             module = module_class.load(os.path.join(model_path, module_config['path']))
    823             modules[module_config['name']] = module
    824 

 ~\anaconda3\envs\py3_nlp\lib\site-packages\sentence_transformers\models\Transformer.py in load(input_path)
     122         with open(sbert_config_path) as fIn:
     123             config = json.load(fIn)
 --> 124         return Transformer(model_name_or_path=input_path, **config)
     125 
     126 

 ~\anaconda3\envs\py3_nlp\lib\site-packages\sentence_transformers\models\Transformer.py in __init__(self, model_name_or_path, max_seq_length, model_args, cache_dir, tokenizer_args, do_lower_case, tokenizer_name_or_path)
     27 
     28         config = AutoConfig.from_pretrained(model_name_or_path, **model_args, cache_dir=cache_dir)
---> 29         self.auto_model = AutoModel.from_pretrained(model_name_or_path, config=config, cache_dir=cache_dir)
    30         self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path if tokenizer_name_or_path is not None else model_name_or_path, cache_dir=cache_dir, **tokenizer_args)
   31 

  ~\anaconda3\envs\py3_nlp\lib\site-packages\transformers\models\auto\auto_factory.py in from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
    393         if type(config) in cls._model_mapping.keys():
    394             model_class = _get_model_class(config, cls._model_mapping)
--> 395             return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
    396         raise ValueError(
    397             f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"

 ~\anaconda3\envs\py3_nlp\lib\site-packages\transformers\modeling_utils.py in from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
    1212                 except Exception:
    1213                     raise OSError(
 -> 1214                         f"Unable to load weights from pytorch checkpoint file for '{pretrained_model_name_or_path}' "
    1215                         f"at '{resolved_archive_file}'"
    1216                         "If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True. "

 OSError: Unable to load weights from pytorch checkpoint file for 'C:\Users\user1/.cache\torch\sentence_transformers\sentence-transformers_bert-base-nli-mean-tokens\' at 'C:\Users\user1/.cache\torch\sentence_transformers\sentence-transformers_bert-base-nli-mean-tokens\pytorch_model.bin'If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True.

短的：

OSError: 无法从 'C:\Users\user1/.cache\torch\sentence_transformers\sentence-transformers_bert-base-nli-mean-tokens' 的 'C:\Users\user1/.cache\ 的 pytorch 检查点文件加载权重torch\sentence_transformers\sentence-transformers_bert-base-nli-mean-tokens\pytorch_model.bin'如果您尝试从 TF 2.0 检查点加载 PyTorch 模型，请设置 from_tf=True。

我在 '.cache\torch\sentence_transformers\sentence-transformers_bert-base-nli-mean-tokens' 文件夹中有 pytorch_model.bin。

为什么我会收到此错误？

4

1 回答 1

0

错误的原因似乎是预训练的模型权重文件不可用或无法加载。

您可以尝试加载预训练模型权重文件：

from transformers import AutoModel
model = AutoModel.from_pretrained('sentence-transformers/bert-base-nli-mean-tokens')

参考：https ://huggingface.co/sentence-transformers/bert-base-nli-mean-tokens

此外，该模型的拥抱脸页面上写着：该模型已弃用。请不要使用它，因为它会产生低质量的句子嵌入。你可以在这里找到推荐的句子嵌入模型：SBERT.net - Pretrained Models

也许你可能想看看。

于 2021-08-05T06:22:33.397 回答