博客

vocab.txt的两种来源

1. 网络

PRETRAINED_VOCAB_ARCHIVE_MAP = {
    'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
    'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
    'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt",
    'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt",
    'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt",
    'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
    'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt",
}

2. 本地

VOCAB_NAME = 'vocab.txt'

3. from_pretrained函数
   
   从代码中可以看到，判断传递的预训练模型地址是否在
   
PRETRAINED_VOCAB_ARCHIVE_MAP

   中，若不在则会将这个路径+
   
VOCAB_NAME

   拼接成vocab.txt的路径

关于-cased：

uncased和cased的区别在于uncased将全部样本变为小写，而cased则要区分大小写，将cased和uncased的模型训练结果进行融合也会有一定程度的提升。

参考资料：

https://blog.csdn.net/weixin_38267719/article/details/94005631

通过

cached_path

函数判断该路径是否是URL（或本地路径），如果是一个URL，下载并缓存文件，然后返回缓存文件的路径。如果已经是本地路径，确保文件存在，然后返回路径。

如果使用的路径在

PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP

中，则会从这里获取模型的

max_len

参数

PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
    'bert-base-uncased': 512,
    'bert-large-uncased': 512,
    'bert-base-cased': 512,
    'bert-large-cased': 512,
    'bert-base-multilingual-uncased': 512,
    'bert-base-multilingual-cased': 512,
    'bert-base-chinese': 512,
}

@classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
        """
        Instantiate a PreTrainedBertModel from a pre-trained model file.
        Download and cache the pre-trained model file if needed.
        """
        if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
            vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
            if '-cased' in pretrained_model_name_or_path and kwargs.get('do_lower_case', True):
                logger.warning("The pre-trained model you are loading is a cased model but you have not set "
                               "`do_lower_case` to False. We are setting `do_lower_case=False` for you but "
                               "you may want to check this behavior.")
                kwargs['do_lower_case'] = False
            elif '-cased' not in pretrained_model_name_or_path and not kwargs.get('do_lower_case', True):
                logger.warning("The pre-trained model you are loading is an uncased model but you have set "
                               "`do_lower_case` to False. We are setting `do_lower_case=True` for you "
                               "but you may want to check this behavior.")
                kwargs['do_lower_case'] = True
        else:
            vocab_file = pretrained_model_name_or_path
        if os.path.isdir(vocab_file):
            vocab_file = os.path.join(vocab_file, VOCAB_NAME)
        # redirect to the cache, if necessary
        try:
            resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
        except EnvironmentError:
            logger.error(
                "Model name '{}' was not found in model name list ({}). "
                "We assumed '{}' was a path or url but couldn't find any file "
                "associated to this path or url.".format(
                    pretrained_model_name_or_path,
                    ', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
                    vocab_file))
            return None
        if resolved_vocab_file == vocab_file:
            logger.info("loading vocabulary file {}".format(vocab_file))
        else:
            logger.info("loading vocabulary file {} from cache at {}".format(
                vocab_file, resolved_vocab_file))
        if pretrained_model_name_or_path in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
            # if we're using a pretrained model, ensure the tokenizer wont index sequences longer
            # than the number of positional embeddings
            max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name_or_path]
            kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
        # Instantiate tokenizer.
        tokenizer = cls(resolved_vocab_file, *inputs, **kwargs)
        return tokenizer

4. cached_path函数

def cached_path(url_or_filename, cache_dir=None):
    """
    Given something that might be a URL (or might be a local path),
    determine which. If it's a URL, download the file and cache it, and
    return the path to the cached file. If it's already a local path,
    make sure the file exists and then return the path.
    """
    if cache_dir is None:
        cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
    if sys.version_info[0] == 3 and isinstance(url_or_filename, Path):
        url_or_filename = str(url_or_filename)
    if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
        cache_dir = str(cache_dir)

    parsed = urlparse(url_or_filename)

    if parsed.scheme in ('http', 'https', 's3'):
        # URL, so get it from the cache (downloading if necessary)
        return get_from_cache(url_or_filename, cache_dir)
    elif os.path.exists(url_or_filename):
        # File, and it exists.
        return url_or_filename
    elif parsed.scheme == '':
        # File, but it doesn't exist.
        raise EnvironmentError("file {} not found".format(url_or_filename))
    else:
        # Something unknown
        raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename))

5. 
python cls(@classmethod)

   
   资料：https://www.cnblogs.com/king-lps/p/12597680.html

cls(resolved_vocab_file, *inputs, **kwargs)

自己调用自己的属性或方法，使用范围上比self小，只能在@classmethod类中使用