Cross-Attention,即交叉注意力机制,是Transformer模型中的另一个重要组件。它在Decoder部分中发挥作用,允许模型在生成输出序列时,将注意力集中在输入序列中的相关部分。这有助于模型更好地理解和生成与输入序列相关的输出序列。 具体而言,Cross-Attention通过计算输入序列和输出序列之间的注意力权重来实现。这些权重表示了...
decoder cross-attention公式 Decoder Cross-Attention是指在Transformer等神经网络模型中,Decoder端使用了Encoder端的信息进行Attention操作,具体公式如下: 假设Decoder端的第i个位置的输入为$q_i$,Encoder端的第j个位置的输出为$k_j$,则Decoder Cross-Attention的计算公式为: 其中,$K$表示Encoder的所有输出,$V$表示...
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https://github.com/pemywei/attention-is-all-you-need-tensorflow/blob/master/Transformer/model/nmt.py https://github.com/tensorflow/models/blob/master/official/nlp/transformer/transformer.py https://towardsdatascience.com/attention-is-all-you-need-discovering-the-transformer-paper-73e5ff5e0634 ...
("bert-base-cased", "bert-base-cased")and fine-tune the model. This means especially the decoder weights have to be adapted a lot, since in the EncoderDecoder framework the model has a causal mask and the cross attention layers are to be trained from scratch. The results so far are ...
8. cross-attention在图文匹配中q和kv分别指什么,在机器翻译中分别指什么9. one shot,zero shot,few shot区别10. 对多模态领域的看法.2️⃣ 大语言模型1. chatGLM和chatGPT区别2. LLAMA的改进点3. LLAMA中RMSNorm比LN的优势4. LLAMA中旋转位置编码5. 除此之外会问还了解什么大语言模型6. 微调的方式(...
The image-recipe cross-modal retrieval task, which retrieves the relevant recipes according to food images and vice versa, is now attracting widespread attention. There are two main challenges for image-recipe cross-modal retrieval task... Z Zhang - 《Mathematics》 被引量: 0发表: 2024年 Cross...
This PR causes models that support cross-attention to output the cross-attention tensors as well as the self-attention tensors when output_attentions is set (from@patrickvonplaten) This PR adds cross-attention outptus to all Seq2Seq models and to CLM models compatible with theEncoderDecoderMod...
Train the model Evaluate a model on the test set Citation Acknowledgment Introduction Cross-modal image-recipe retrieval has gained significant attention in recent years. Most work focuses on improving cross-modal embeddings using unimodal encoders, that allow for efficient retrieval in large-scale data...