预训练模型通常在大规模的无标注文本数据上进行训练,学习到词与词之间的复杂关系,从而生成高质量的词向量。BERT将作为优化本模型所采用的预训练模型,且我们选择的版本是轻量级的bert-base-uncased。 由于使用预训练的模型作为词嵌入,数据预处理的代码需要重写,我们将不必为文本数据建立词汇表,而是直接把文本样本交给导入...
self.bert = BertModel.from_pretrained('bert-base-uncased') self.linear = nn.Linear(self.bert.config.hidden_size, 512) # 假设我们的特征空间大小为512 def forward(self, input_ids, attention_mask): outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask) pooled_output = outpu...
下面的代码使用LoRA在分类任务上训练了bert-base-uncased模型: importos os.environ['CUDA_VISIBLE_DEVICES'] ='0'frommodelscopeimportAutoModelForSequenceClassification, AutoTokenizer, MsDatasetfromtransformersimportdefault_data_collatorfromswiftimportTrainer, LoRAConfig, Swift, TrainingArguments model = AutoModelF...
例如:https://huggingface.co/bert-base-uncased 2.加载本地onnx模型,并进行推测。 String text = "Let's demonstrate that embedding can be done within a Java process and entirely offline."; // path "C:/Users/laker/Downloads/model.onnx" EmbeddingModel embeddingModel = new OnnxEmbeddingModel("/...
self.bert=BertModel.from_pretrained('bert-base-uncased')self.linear=nn.Linear(self.bert.config.hidden_size,512)# 假设我们的特征空间大小为512defforward(self,input_ids,attention_mask):outputs=self.bert(input_ids=input_ids,attention_mask=attention_mask)pooled_output=outputs.pooler_outputreturnself....
# 更多可选择模型: # ['bert-base-uncased', 'bert-large-uncased', 'bert-base-multilingual-uncased', 'bert-base-cased', 'bert-base-chinese', 'bert-base-multilingual-cased' # , 'bert-large-cased', 'bert-wwm-chinese', 'bert-wwm-ext-chinese', 'macbert-base-chinese', 'macbert-large-chin...
def get_cls_task(module_name, max_seq_len, batch_size): ''' func: 配置hub.TextClassifierTask return: 返回配置好的hub.TextClassifierTask params: module_name: 模型名称,['ernie_v2_eng_large', 'ernie_v2_eng_base', 'bert_uncased_L-24_H-1024_A-16', 'bert_uncased_L-12_H-768_A-12...
The uncased model increases the ambiguity. Despite this mixture of entity types in the vocabulary vector space, the learned transformation clearly separates these sense in the models output for a masked position in a sentence using the sentence context. for example, the blank position in the first...
They fine-tuned the uncased version of the DitlBeERT model on a dataset for training and testing only. In their efforts, they also created a classifier that relies on the perplexity score. This involved calculating the perplexity score of each entry in the training set using the GPT-2 model...
As a comparison, a conventional bert-base-uncased model limits the input length to only 512 tokens. In Reformer, each part of the standard transformer architecture is re-engineered to optimize for minimal memory requirement without a significant drop in performance. The memory improvements can...