Just below that section replace the following: # support checkpoint without position_ids (invalid checkpoint) if "text_model.embeddings.position_ids" not in text_model_dict: text_model_dict["text_model.embeddings.position_ids"] = torch.arange(77).unsqueeze(0) # 77 is the max length of the...
%/text_model/embeddings/Slice_output_0 = Slice(%text_model.embeddings.position_ids, %/text_model/embeddings/Constant_1_output_0, %/text_model/embeddings/Unsqueeze_output_0, %/text_model/embeddings/Constant_3_output_0, %/text_model/embeddings/Constant_4_output_0) %/text_model/embeddings/toke...
input_shape = input_ids.size() = torch.Size([1,77]) 接下来调用一次embeddings获得网络层的输出 hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids) 这里position_ids为None,进入到CLIPTextEmbeddings的forward函数中 seq_length = input_ids.shape[-1] if input_ids is not...
代码:embeddings-benchmark/mteb :大规模文本嵌入评估 中文文本嵌入评估:CMTEB 向量的检索 向量搜索库 Approximate Nearest Neighbor(ANN)是一种用于在大规模数据集中寻找最近邻居的算法。其目标是在尽可能短的时间内找到与给定查询点最近的数据点,但不一定是确切的最近邻。为了达到这个目标,ANN使用了一些启发式方法,例...
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for ControlLDM: Unexpected key(s) in state_dict: "cond_stage_model.transformer.text_model.embeddings.position_ids"....
embeddings, int embStart, int paragraphLength, int flags); Parameters text Char[] an array containing the paragraph of text to process. textStart Int32 the index into the text array of the start of the paragraph. embeddings Byte[] an array containing embedding values for each character ...
如果用词,提前分好词,词之间用空格隔开,python run.py --model TextCNN --word True 使用预训练词向量:utils.py的main函数可以提取词表对应的预训练词向量。 实验效果 机器:一块2080Ti , 训练时间:30分钟。 原始的bert效果就很好了,把bert当作embedding层送入其它模型,效果反而降了,之后会尝试长文本的效果对比...
combining word embeddings using the SapBERT model on regulatory documents. These embeddings are put through a critical hierarchical agglomerative clustering step, and the clusters are organized through a custom data structure. Each cluster is summarized using the bart-large-cnn-samsum model, and each ...
self.bert = BertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) self.init_weights() def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, input...
",\"pooling_method\":\"MEAN\",\"normalize_result\":false},\"load_model\":true,\"model_node_ids\":[\"modelNodeIds\"]}"; private final FunctionName functionName = FunctionName.LINEAR_REGRESSION; private final String modelName = "modelName"; private final String version = "version"; Ex...