出现错误 "unexpected key(s) in state_dict: 'text_model.embeddings.position_ids'" 通常意味着在尝试加载模型状态字典时,状态字典中包含了一些当前模型定义中不存在的键。 原因分析 模型版本不匹配: 你可能正在尝试将一个使用不同版本训练的模型状态字典加载到一个新版本的模型中。不同版本的模型可能在架构上有...
text_model_dict["text_model.embeddings.position_ids"] = torch.arange(77).unsqueeze(0) # 77 is the max length of the text with # remove position_ids for newer transformer, which causes error :( if "text_model.embeddings.position_ids" in text_model_dict: text_model_dict.pop("text_model...
if "text_model.encoder.text_model.embeddings.position_ids" in new_sd: # waifu diffusion v1.4 position_ids = new_sd[ANOTHER_POSITION_IDS_KEY] del new_sd[ANOTHER_POSITION_IDS_KEY] else: position_ids = torch.Tensor([list(range(max_length))]).to(torch.int64) new_sd.pop("text_model.enc...
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...
%/text_model/embeddings/Constant_3_output_0 = Constant[value = <Tensor>]() %/text_model/embeddings/Constant_4_output_0 = Constant[value = <Tensor>]() %/text_model/embeddings/Slice_output_0 = Slice(%text_model.embeddings.position_ids, %/text_model/embeddings/Constant_1_output_0, %/tex...
If corpus size exceeds these limits, you could use a SageMaker training job, which generates embeddings for your large dataset and persists them alongside the model inside the SageMaker endpoint. Therefore, they don’t have to be passed as part of the invocation payload. ...
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 ...
Since the transformer model does not dis- tinguish between the rows of this embedding, i.e., the posi- tion of each token in the sequence, we need to encode the positions separately. Analogously to vocabulary embeddings we create position embeddings from position IDs Pin and a position ...
TextModelWithProjection, AutoTokenizer, ) PROJECTION_DIM = 512 MAX_POSITION_EMBEDDINGS = max_length_string + 1 textConfig = CLIPTextConfig.from_pretrained("openai/clip-vit-base-patch32") textConfig.projection_dim = PROJECTION_DIM textConfig.max_position_embeddings = MAX_POSITION_EMBEDDINGS model ...
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 ...