self.blks = nn.Sequential() for i in range(num_layers): self.blks.add_module("block"+str(i), EncoderBlock(key_size, query_size, value_size, num_hiddens, norm_shape, ffn_num_input, ffn_num_hiddens, num_heads, dr
File "/Users/jiuleyang/Projects/trans/textclassify.py", line 2, in <module> model = AutoModelForSequenceClassification.from_pretrained('uer/roberta-base-finetuned-chinanews-chinese') File "/Users/jiuleyang/anaconda3/envs/transformers/lib/python3.9/site-packages/transformers/models/auto/auto_facto...
如果 tokenize_chinese_chars 为 True,则会在每个中文“字”的前后增加空格,然后用whitespace_tokenize()进行 tokenization,因为增加了空格,空白符又都统一换成了空格,实际上 whitespace_tokenize() 就是用了 Python 自带的 split() 函数,处理前用先 strip() 去除了文本前后的空白符。whitespace_tokenize() 的函数内...
Module): def __init__(self, trg_vocab_size, embed_size, heads, forward_expansion, dropout, n_layers, max_len, device) : super(Decoder,self).__init__() #和Encoder基本一致 # --- self.embed_size = embed_size self.device = device self.word_embedding = nn.Embedding(trg_vocab_size,...
等待一段时间,pip将自动从PyPI(Python包索引)下载并安装transformers库及其依赖项。 3. 在您的Python代码中导入transformers库: ```python from transformers import <module_name> ``` 这里的`<module_name>`是您所需要的具体模块名称,例如`BertModel`, `GPT2Tokenizer`等。根据您的具体需求,导入相应的模...
Module): def __init__(self, bert, hidden_dim, output_dim, n_layers, bidirectional, dropout): super().__init__() self.bert = bert embedding_dim = bert.config.to_dict()['hidden_size'] self.rnn = nn.GRU(embedding_dim, hidden_dim, num_layers = n_layers, bidirectional = ...
freezed_parameters=[]forname,parameterinmodule.named_parameters():ifnot parameter.requires_grad:freezed_parameters.append(name)returnfreezed_parameters 代码语言:javascript 代码运行次数:0 运行 AI代码解释 importtorch from transformersimportAutoConfig,AutoModel ...
transformers : 4.41.1, 4.41.2 python : 3.12.3 platform: ubuntu linux 22.04 x64 Who can help? No response Information The official example scripts My own modified scripts Tasks An officially supported task in the examples folder (such as GLUE/SQuAD, ...) My own task or dataset (give de...
模型本身是一个常规的Pytorchnn.Module或TensorFlowtf.keras.Model(取决于你的后端),可以常规方式使用。这个教程解释了如何将这样的模型整合到经典的 PyTorch 或 TensorFlow 训练循环中,或是如何使用我们的Trainer训练器)API 来在一个新的数据集上快速微调。
import torchimport torch.nn as nnimport torch.nn.functional as Ffrom torch.optim import OptimizerKD_loss = nn.KLDivLoss(reduction='batchmean')def kd_step(teacher: nn.Module,student: nn.Module,temperature: float,inputs: torch.tensor,optimizer: Optimizer):teacher.eval()student.train()with torch...