首先,先获得一个PreTrainedTokenizer类。 fromtransformersimportBertTokenizer TOKENIZER_PATH="../input/huggingface-bert/bert-base-chinese"tokenizer=BertTokenizer.from_pretrained(TOKENIZER_PATH) tokenizer有一个方法tokenize,它会将我们的输入句子进行分词操作, 而convert_tokens_to_ids会将一个句子分词得到的tokens映射...
tokenizer 的加载和保存和 models 的方式一致,都是使用方法:from_pretrained,save_pretrained. 这个方法会加载和保存tokenizer使用的模型结构(例如sentence piece就有自己的模型结构),以及字典。 下面是一个使用的example: fromtransformersimportBertTokenizertokenizer=BertTokenizer.from_pretrained("bert-base-cased",use_fa...
BertTokenizer 加载编码器,当然用AutoTokenizer也可以 from transformers import BertTokenizer,AutoTokenizer tokenizer = BertTokenizer.from_pretrained('bert-base-chinese') tokenize 将句子拆分为token,并不映射为对应的id from transformers import BertTokenizer,AutoTokenizer tokenizer = BertTokenizer.from_pretrained('...
NotImplementedError: return_offset_mapping is not available when using Python tokenizers. To use this feature, change your tokenizer to one deriving from transformers.PreTrainedTokenizerFast. More information on available tokenizers at https://github.com/huggingface/transformers/pull/2674 Output is truncat...
import torchfrom transformers import *#Transformers has a unified API#for 8 transformer architectures and 30 pretrained weights. #Model | Tokenizer | Pretrained weights shortcutMODELS = [(BertModel, BertTokenizer, 'bert-base-uncased'), (OpenAIGPTModel, OpenAIGPTTokenizer, 'openai-gpt'), (GPT2...
tokenizer = AutoTokenizer.from_pretrained("t5-base") model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") encoder_input_str ="translate English to German: How old are you?" input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids ...
def main(dataset: Dataset, tokenizer_name: str = "t5-base", batch_size: int = 1024): tokenizer = T5Tokenizer.from_pretrained(tokenizer_name) for datafile in (f"{dataset.value}_train.csv", f"{dataset.value}_valid.csv", f"{dataset.value}_test.csv"): if not (DATA_PATH / datafile)...
from transformers import * #Transformers has a unifiedAPI #for 8 transformer architectures and 30 pretrained weights. #Model | Tokenizer | Pretrained weights shortcut MODELS = [(BertModel, BertTokenizer, 'bert-base-uncased'), (OpenAIGPTModel, OpenAIGPTTokenizer, ...
from_dict:由一个参数字典构建Config; from_json_file:由一个参数json文件构建Config; from_pretrained:由一个预训练的模型配置实例化一个配置 2. BertTokenizer 以字分割,继承PreTrainedTokenizer,前面介绍过,构造函数参数; vocab_file(string):字典文件,每行一个wordpiece ...
from_pretrained()方法 要加载Google AI、OpenAI的预训练模型或PyTorch保存的模型(用torch.save()保存的BertForPreTraining实例),PyTorch模型类和tokenizer可以被from_pretrained()实例化: model = BERT_CLASS.from_pretrained(PRE_TRAINED_MODEL_NAME_OR_PATH, cache_dir=None, from_tf=False, state_dict=None, *...