步骤1:安装Transformers库 首先,我们需要安装Transformers库。你可以在终端中运行以下命令: pipinstalltransformers 1. 该命令使用pip安装Transformers库,以便我们在Python中进行使用。 步骤2:导入所需的包 在代码中使用Transformers库,首先需要导入相关的模块: fromtransformersimportAutoTokenizer,AutoModelForSequenceClassificatio...
from transformers import AutoTokenizer, AutoModelForCausalLM import torch 这里我们导入了Flask来创建Web应用,以及transformers库中用于加载和使用预训练模型的相关模块。torch则是PyTorch的核心包,提供了张量运算和其他深度学习特性。 创建Flask应用实例 app = Flask(__name__) 这行代码创建了一个新的Flask应用实例,_...
使用AutoModel和AutoTokenizer按需加载模型: from transformers import AutoTokenizer, AutoModelForSeq2SeqLM # 加载翻译模型(英文→法文) model_name = "Helsinki-NLP/opus-mt-en-fr" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) 1. 2. 3...
# 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, AutoModel import, json, datetime import torch DEVICE = "cuda" DEVICE_ID = "0" CUDA_DEVICE = f"{DEVICE}:{DEVICE_ID}" if DEVICE_ID else DEVICE def torch_gc(): if torch.cuda.is_available(): ...
from transformers import FNetTokenizerFast, CamembertTokenizerFast, \ BertTokenizerFast # Text to normalize text = 'ThÍs is áN ExaMPlé sÉnteNCE' # Instantiate tokenizers FNetTokenizer = FNetTokenizerFast.from_pretrained('google/fnet-base') ...
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...
pip install transformerspip install requests 加载预训练模型 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,...
from transformersimportAutoTokenizer,AutoModel defmain():"""使用插件时回复文字"""DEVICE='cuda'iftorch.cuda.is_available()else'cpu'tokenizer=AutoTokenizer.from_pretrained('/home/chatglm3-6b',trust_remote_code=True)model=AutoModel.from_pretrained('/home/chatglm3-6b',trust_remote_code=True).to...
fromtransformersimportAutoTokenizer # 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 \ ...