nn.functional as F #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(...
1 单模态:文本识别与生成 文本生成模型中,与靠实力大火的 GPT 族相比,其他很多早期的研究就略显暗淡。所以这里主要介绍 GPT 族模型及其相关研究。 重点论文解读 GPT123:GPT-1/GPT-2/GPT-3 简介 GPT123:GPT,GPT-2,GPT-3 论文精读【论文精读】 InstructGPT:OpenAI 是如何“魔鬼调教” GPT 的?——InstructGPT...
AI检测代码解析 fromPILimportImageimporttorchimporttorchvision.transformsastransforms# 加载图像和转换defload_image(file_path):image=Image.open(file_path)transform=transforms.Compose([transforms.Resize((512,512)),transforms.ToTensor(),transforms.Normalize(mean=[0.485,0.456,0.406],std=[0.229,0.224,0.225])]...
self).__init__()self.main=nn.Sequential(nn.Linear(100,256),nn.ReLU(True),nn.Linear(256,512),nn.ReLU(True),nn.Linear(512,1024),nn.Tanh())defforward(self,input):returnself.main(input)# 定义判别器classDiscriminator(nn.Module
# (bs, h)h_k_avg = self.masked_avgpool(sequence_output, attention_mask)# (bs, rel_num)rel_pred = self.rel_judgement(h_k_avg)loss_func = nn.BCEWithLogitsLoss(reduction='mean')loss_rel = loss_func(rel_pred, rel_tags.float())在预测阶段，通过sigmoid函数获取句子对应的所有关系标签的...
{ "height": 224, "width": 224 }, "do_center_crop": true, "do_convert_rgb": true, "do_normalize": true, "do_rescale": true, "do_resize": true, "feature_extractor_type": "CLIPFeatureExtractor", "image_mean": [ 0.48145466, 0.4578275, 0.40821073 ], "image_processor_type": "CLIP...
D. People spend more time in reading today. 3. What does the word “integration” in the last paragraph probably mean? A. separation B. cooperation C. competition D. appreciation 4. What is the author’s purpose in writing the article? A. To introduce the development of Chinese literature...
移植text_encoder 这里可以将text_encoder模型转为onnx模型,然后构造转换脚本,将onnx模型转换为bmodel模型。 转换为onnx模型defexport_textencoder(pipe): forparainpipe.text_encoder.parameters(): para.requires_grad =False batch =1 fake_input = torch.randint(0,1000, (batch,77)) ...
What does the underlined word "produce" mean in Chinese? A.生产 B.引起 C.栽培 D.生长)49. What don't the writer's uncle and aunt have? A. B. C. D.( )50. What is the text about? A. My uncle's garden. B. My uncle's farm. C. My uncle's vegetables. D. My uncle's ...
论文对Subecjt在词上面做mean pooling,然后拼接到指定Predict的Object抽取的token上面,从而完成Subject到Predict和Object的映射。 而现在大家更常用的方式是Conditional Layer Normalization(CLN),详见基于Conditional Layer Normalization的条件文本生成 个人观点: 优点: 能解决SEO/EPO/SOO重叠问题。 缺点: 计算效率低,有很多...