使用from_pretrained()函数加载模型需要pytorch_model.bin和config.json文件。 加载tokenizer 测试代码:如果加载成功,就打印1。 fromtransformersimportAutoTokenizer tokenizer = AutoTokenizer.from_pretrained("./bert-base-chinese")print(1)
但这种方法有个缺点,就是访问http://huggingface.co不稳定,时通时断,还出现过由于huggingface长时间不通导致应用无法起动的悲剧,所以这种方法在实际生产过程中很少采用,一般都是将模型下载到本地目录后装载,类似于: model = AutoModel.from_pretrained("./models/THUDM/chatglm-6b").half().cuda() 所以如何快速...
1. 使用配置对象的方式:例: config = AutoConfig.from_pretrained(model_path, trust_remote_code=True, pre_seq_len=128) model = AutoModel.from_pretrained(model_path, config=config, trust_remote_code=Tr…
from_pretrained("/root/onnx/model/huggingface/pegasus-newsroom") Some weights of the model checkpoint at /root/onnx/model/huggingface/pegasus-newsroom were not used when initializing PegasusModel: ['final_logits_bias'] - This IS expected if you are initializing PegasusModel from the checkpoint of...
from_pretrained(model_id) 用bert的方法对数据集做分词预处理,把所有序列补充或截断到256个token 代码语言:javascript 代码运行次数:0 运行 AI代码解释 MAX_LENGTH = 256 train_dataset = train_dataset.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length', max_length=MAX_LENGTH),...
model = BertModel.from_pretrained("bert-base-uncased")# ...一系列操作text ="Replace me by any text you'd like."encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input)print(output) 方法二:网址下载
4. Model Loading Load a pre-trained model corresponding to your task. For instance, if you’re performing text classification: model = AutoModel.from_pretrained("bert-base-uncased") 5. Inference Pipeline Transformers provide high-level pipelines for various tasks like text generation, translation,...
>>> from transformers import BertModel>>> model = BertModel.from_pretrained("bert-base-chinese") BertModel是一个PyTorch中用来包裹网络结构的torch.nn.Module,BertModel里有forward()方法,forward()方法中实现了将Token转化为词向量,再将词向量进行多...
save_pretrained('my-model-library', tokenizer=tokenizer, model=model) 上传到Hugging Face: 最后,您需要将打包的模型库上传到Hugging Face。首先,您需要在Hugging Face上创建一个新的模型库,然后使用transformers库中的push_to_hub方法将模型库推送到您的Hugging Face仓库。以下是一个示例: from transformers import...
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True) def tokenize_function(examples): return tokenizer(examples["text"]) tokenized_datasets = datasets.map(tokenize_function, batched=True, num_proc=4, remove_columns=["text"]) ...