checkpoint = 'fnlp/bart-base-chinese' tokenizer = MyCustomTokenizerFast.from_pretrained('myusername/mytokenizer') model = BartForConditionalGeneration.from_pretrained(checkpoint, output_attentions = True, output_hidden_states = True) # this one has to change?! In theory, a tok...
# self.tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, device_map="cpu", trust_remote_code=True) # CPU方式加载模型 # self.model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path, device_map="cpu", trust_remote_code=True) # CPU方式加载模型 self....
You cannot use your own pretrained tokenizer for a pretrained model. The reason is that the vocabulary for your tokenizer and the vocabulary of the tokenizer that was used to pretrain the model that later you will use it as pretrained model are different. Thus a word-piece ...
app = Flask(__name__)classQwenModel:def__init__(self, pretrained_model_name_or_path):# self.tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, device_map="cpu", trust_remote_code=True) # CPU方式加载模型# self.model = AutoModelForCausalLM.from_pretrained(pretrained_m...
What am I doing wrong? I encode data with model_name = "dbmdz/bert-base-italian-uncased" tokenizer = AutoTokenizer.from_pretrained(model_name, do_lower_case = True) def encode_data(texts): return tokenizer.batch_encode_plus( texts, add_s...
Hello, I'm tring to train a new tokenizer on my own dataset, here is my code: from tokenizers import Tokenizer from tokenizers.models import BPE from tokenizers.trainers import BpeTrainer unk_token = '<UNK>' spl_tokens = ['<UNK>', '<SEP>', '<MASK>', '<CLS>'] ...
getLogger(__name__) def initialize_model_and_tokenizer(model_name, model_kwargs): """ Initialize the model and tokenizer with the given pretrained model name and arguments. """ model = AutoModelForCausalLM.from_pretrained(model_name, **model_kwargs) tokenizer =...
You can use the same type of tokenizer as the 'flavor' of model your transfer learning is based upon and train your own tokenizer. See the video here: Training a new tokenizer from an old one. Regarding OOV tokens, there is also utility method that might be worth looking into, add_tok...
ChatModel– This class loads the model and tokenizer and generates the response. It handles partitioning the model across multiple GPUs usingtensor_parallel_degree, and configures thedtypesanddevice_map. The prompts are passed to the model to generate responses. A stopping...
from transformers import PreTrainedTokenizer, PreTrainedModel, PretrainedConfig class MyTokenizer(PreTrainedTokenizer): def __init__(self, vocab_file, **kwargs): super().__init__(vocab_file, **kwargs) def __call__(self, text): tokens = text.split() ...