Apr 12, 2023 · I am trying to create an encoder-decoder-model, which encodes an 10x10 list and should decode it to an 3x8x8 array/list. Which loss function should I choose to achieve this? I know that the shapes ...

EncoderDecoderModel can be randomly initialized from an encoder and a decoder config. In the following example, we show how to do this using the default BertModel configuration for the encoder and the default BertForCausalLM configuration for the decoder. Initialising EncoderDecoderModel from a pretrained encoder and a pretrained decoder.

A Sequence to Sequence network, or seq2seq network, or Encoder Decoder network, is a model consisting of two RNNs called the encoder and decoder. The encoder reads an input sequence and outputs a single vector, and the decoder reads that vector to produce an output sequence.

Jul 8, 2021 · Let’s begin by creating an instance of our model, loss function, and optimizer. We will use the Stochastic Gradient Descent optimizer , the Cross-Entropy Loss function , and a learning rate of 0.01.

May 2, 2022 · First, I encode the dataset of Domain B using (AE_model.encoder). Then, my idea was to have new_model to predict encoded dataset of Domain B, then have AE_model.decoder to decode back to the original state. When I tried using a Loss function of: encoded_prediction = new_model(X_valid) Loss(encoded_prediction, encoded_GT) it works fine.

Nov 14, 2024 · What is the difference between an encoder-decoder transformer and decoder-only transformer with regard to the loss calculation. Specifically, how does the loss signal differ? And how does this relate to token efficiency?

The EncoderDecoderModel can be used to initialize a sequence-to-sequence model with any pretrained autoencoding model as the encoder and any pretrained autoregressive model as the decoder.

Mar 12, 2021 · the EncoderDecoder model calculates the standard auto-regressive cross-entropy loss using the labels i.e the output sequence. It just shifts the labels inside the models before computing the loss. It’s the same loss used in other seq2seq models like BART, T5, and decoder models like GPT2. Hope this helps.

Mar 26, 2025 · 7. Transformer Model. This block defines the main Transformer class which combines the encoder and decoder layers. It also includes the embedding layers and the final output layer. self.encoder_embedding = nn.Embedding(src_vocab_size, d_model): Initializes the embedding layer for the source sequence, mapping tokens to continuous vectors of size ...

Feb 17, 2022 · I am attempting to create an encoder/decoder model with mini-batch. I continue to encounter an errors stating: RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [32, 6]], which is output 0 of AsStridedBackward0, is at version 2; expected version 1 instead.