"vocab_file": { "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt", } } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "bert-base-uncased": 512, } PRETRAINED_INIT_CONFIGURATION = { "bert-base-uncased": {"do_lower_case": True}, } def load_vocab(vocab...
type=str,default="../dataset/bert-base-uncased")# 使用时是en_bert_file_path# 中文bert路径parser.add_argument('--zh-bert-file-path',type=str,default="../dataset/bert-base-chinese")# 使用时是zh_bert_file_pathopt=parser.parse_args()print("参数初始化成功")return...
Batch size = 1 PreTrainedTokenizer(name_or_path='D:\team_code\dataset\pre_triained_model\bert-base-uncased', vocab_size=30522, model_max_len=1000000000000000019884624838656, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': ...
I am trying to execute : import ktrain from ktrain import text MODEL_NAME='distilbert-base-uncased' t=text.Transformer(MODEL_NAME, maxlen=500, classes=np.unique(y_train)) I am getting the following error: *OSError: Model name 'distilbert-base-uncased' was not found in tokenize...
uncased/pytorch_model.bin'bert_config_file=bert_bin_dir+"config.json"tokenizer=FullTokenizer(vocab_file=bert_bin_dir+'vocab.txt')bertmodel,bertconfig,get_data=get_model_function('bert-base')withopen(bert_config_file,'r',encoding='utf8')asfp:json_data=json.load(fp)print(json_data)config...
下载完成后,按照config.json,vocab.txt,pytorch_model.bin重命名,放在bert-base-uncased文件夹下,此例中bert-base-uncased文件夹放置在项目根目录下 如果是处理中文任务,把链接中的bert-base-uncased替换成bert-base-chinese即可,存放文件夹名可根据习惯修改为相应模型的名称 ...
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm...
tokenizer.vocab_size model config tokenizer.model_max_length # tokenizer和模型要意义对应,给定模型输入的最大长度和输入名字 tokenizer.model_input_names 注:distil是蒸馏的意思 distilbert 是对 bert 的 distill 而来,其模型结构更为简单, bert-base-uncased 参数量:109482240 distilbert-base-uncased 参数量:...
In this case, the error is because the cached path for the url https://huggingface.co/google/bert_uncased_L-2_H-128_A-2/resolve/main/added_tokens.json cannot be found in the cache when local_files_only=True. This is because the URL 404s; i.e., the file does not exist. When ...
This will use the Bert-base-uncased model, which has a small representation. The docker run also accepts a variety of arguments for custom and different models. This can be done through a command such as: docker build -t summary-service -f Dockerfile.service ./ docker run --rm -it -p...