Copy# Load the GPT-2 tokenizertokenizer = AutoTokenizer.from_pretrained("gpt2")# Example texttext = "Hello, world!"# Tokenization processtokens = tokenizer.tokenize(text) # Converts text into tokenstoken_ids = tokenizer.convert_tokens_to_ids(tokens) # Converts tokens into numerical IDs# Outp...
self.model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.float32, device_map='cpu', trust_remote_code=True # 允许执行自定义代码 ) self.model = self.model.eval() def chat(self, user_input): inputs = self.tokenizer(user_input, return_tensors="pt", padding=True...
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 = AutoTokenizer.from_pretrained("bert-base-chinese") model = AutoModel.from_pretrained("bert-base-chinese...
spaces, and " \ "punctuation.") # Instatiate the pre-tokenizers GPT2_PreTokenizer = AutoTokenizer.from_pretrained('gpt2').backend_tokenizer \ .pre_tokenizer Albert_PreTokenizer = AutoTokenizer.from_pretrained('albert-base-v1') \ .backend_tokenizer.pre_tokenizer...
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, use_cache = False, device_map=device_map) model.config.pretraining_tp = 1 # Load the tokenizer tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) ...
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, use_cache = False, device_map=device_map) model.config.pretraining_tp = 1 # Load the tokenizer tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) ...
tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2) def preprocess_function(examples): return tokenizer(examples['text'], padding='max_length', truncation=True) ...
AutoTokenizer.from_pretrained:加载预训练模型的分词器。 AutoModelForCausalLM.from_pretrained:加载预训练的生成模型。 4. 微调模型 我们将使用数据集对模型进行微调。假设我们已经将数据分为训练集和验证集,并将其转换为模型可以使用的格式: fromdatasetsimportDataset# 将DataFrame转换为Dataset对象dataset=Dataset.from...
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...
model = AutoModel.from_pretrained("dbmdz/bert-base-italian-xxl-cased") # 2D array, one line per sentence containing the embedding of the first token encoded_sentences = torch.stack([model(**tokenizer(s, return_tensors='pt'))[0][0][0] ...