相信很多小伙伴平时都使用内网进行工作，这些网络是无法连接huggingface的，使用魔塔加载模型网络断断续续的很容易失败。但是bert_score只接收一个模型名，然后自动在huggingface下载或在本地缓存加载。这个缓存跟huggingface官方缓存是不同的。

解决办法1：修改bert_score源码。bert_score虽说是只接受模型名，但内部还是通过AutoTokenizer.from_pretrained和AutoModel.from_pretrained这两个方法加载模型，相信这两个方法大家都很熟悉了。因此只需要在源码中添加自己的model_path，并且把源码中的model_type这个参数改为model_path

源码：

def get_model(model_type, num_layers, all_layers=None):
    if model_type.startswith("scibert"):
        model = AutoModel.from_pretrained(cache_scibert(model_type))
    elif "t5" in model_type:
        from transformers import T5EncoderModel

        model = T5EncoderModel.from_pretrained(model_type)
    else:
        model = AutoModel.from_pretrained(model_type)
    model.eval()

def get_tokenizer(model_type, use_fast=False):
    if model_type.startswith("scibert"):
        model_type = cache_scibert(model_type)

    if version.parse(trans_version) >= version.parse("4.0.0"):
        tokenizer = AutoTokenizer.from_pretrained(model_type, use_fast=use_fast)
    else:
        assert not use_fast, "Fast tokenizer is not available for version < 4.0.0"
        tokenizer = AutoTokenizer.from_pretrained(model_type)

    return tokenizer

改为：

def get_model(model_type, num_layers, all_layers=None):
    model_path = 'xxx'
    if model_type.startswith("scibert"):
        model = AutoModel.from_pretrained(cache_scibert(model_type))
    elif "t5" in model_type:
        from transformers import T5EncoderModel

        model = T5EncoderModel.from_pretrained(model_path)
    else:
        model = AutoModel.from_pretrained(model_path)
    model.eval()


def get_tokenizer(model_type, use_fast=False):
    if model_type.startswith("scibert"):
        model_type = cache_scibert(model_type)

    model_path = 'xxx'
    if version.parse(trans_version) >= version.parse("4.0.0"):
        tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=use_fast)
    else:
        assert not use_fast, "Fast tokenizer is not available for version < 4.0.0"
        tokenizer = AutoTokenizer.from_pretrained(model_path)

    return tokenizer

解决办法2：让bert_score找到缓存模型，相信聪明的小伙伴已经在前面的代码中看到bert_score是如何加载缓存模型的。如果要加载缓存模型，model_type字段加载的模型前要加scibert-前缀。并且需要把本地模型放在指定的目录下。可以看出这个函数下载的模型有它自己的命名规则，需要根据它的规则对自己的模型文件做出相应修改。

下面给出部分源码

def cache_scibert(model_type, cache_folder="~/.cache/torch/transformers"):
    if not model_type.startswith("scibert"):
        return model_type

    underscore_model_type = model_type.replace("-", "_")
    cache_folder = os.path.abspath(os.path.expanduser(cache_folder))
    filename = os.path.join(cache_folder, underscore_model_type)