例如BERT、GPT-3等。如果你输入的模型名称与官方库中的名称不符，AutoModel和AutoTokenizer将无法找到匹配...
以下是使用BERT模型计算两个句子相似度的Python完整实现示例,需要安装 transformers库:from transformers import AutoTokenizer, AutoModel import torch import numpy as np # 加载BERT模型和分词器 tokenizer …
Albert_PreTokenizer=AutoTokenizer.from_pretrained('albert-base-v1')\.backend_tokenizer.pre_tokenizer # Pre-tokenize the textprint('GPT-2 Pre-Tokenizer:')print_pretokenized_str(GPT2_PreTokenizer.pre_tokenize_str(text))#GPT-2Pre-Tokenizer:#"this","Ġsentence","'s","Ġcontent","Ġinclud...
GPT2_PreTokenizer = AutoTokenizer.from_pretrained('gpt2').backend_tokenizer \ .pre_tokenizer Albert_PreTokenizer = AutoTokenizer.from_pretrained('albert-base-v1') \ .backend_tokenizer.pre_tokenizer # Pre-tokenize the text print('GPT-2 Pre-Tokenizer:') print_pretokenized_str(GPT2_PreTokenizer...
tokenizer=AutoTokenizer.from_pretrained("THUDM/chatglm3-6b",trust_remote_code=True)model=AutoModel.from_pretrained("THUDM/chatglm3-6b",trust_remote_code=True,device='cuda')model=model.eval()response,history=model.chat(tokenizer,"你好",history=[])print(response) ...
from_pretrained(model_id, quantization_config=bnb_config, use_cache = False, device_map=device_map)model.config.pretraining_tp = 1# Load the tokenizertokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)tokenizer.pad_token = tokenizer.eos_tokentokenizer.padding_...
tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModel.from_pretrained(model_name) # 示例文本数据 text1 = "Python is a popular programming language" text2 = "Java is another widely used language" # 对文本进行分词和编码 ...
# 文本解码 将 token IDs 转换回原来的字符串# 可以使用 tokenizer 将 input_ids 解码为原始输入fromtransformersimportBertTokenizer tokenizer=AutoTokenizer.from_pretrained("bert-base-cased")text="here is some text to encode"encoded_input=tokenizer(text,return_tensors='pt')print(encoded_input)# ---#...
from transformers import AutoTokenizer, AutoModelForCausalLMtokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")定义生成函数 def generate_text(prompt, model, tokenizer, length=500, temperature=0.7): input_ids ...
from transformers import AutoTokenizer # Text to pre-tokenize text = ("this sentence's content includes: characters, spaces, and " \ "punctuation.") # Instatiate the pre-tokenizers GPT2_PreTokenizer = AutoTokenizer.from_pretrained('gpt2').backend_tokenizer \ .pre_tokenizer Albert_PreTokenizer...