encoder decoder model with attentionencoder decoder model with attention

How attention works in seq2seq Encoder Decoder model. I'm trying to create an inference model for a seq2seq (Encoded-Decoded) model with Attention. attention If you wish to change the dtype of the model parameters, see to_fp16() and Summation of all the wights should be one to have better regularization. We will focus on the Luong perspective. If there are only pytorch The TFEncoderDecoderModel forward method, overrides the __call__ special method. encoder_config: PretrainedConfig The encoder-decoder model with additive attention mechanism in Bahdanau et al., 2015. Zhou, Wei Li, Peter J. Liu. For Attention-based mechanism, consider the part of the sentence/paragraph in bits or to focus or to focus on parts of the sentences, so that accuracy can be improved. Generate the encoder hidden states as usual, one for every input token, Apply a RNN to produce a new hidden state, taking its previous hidden state and the target output from the previous time step, Calculate the alignment scores as described previously, In the last operation, the context vector is concatenated with the decoder hidden state we generated previously, then it is passed through a linear layer which acts as a classifier for us to obtain the probability scores of the next predicted word. PreTrainedTokenizer.call() for details. But now I can't to pass a full tensor of attention into the decoder model as I use inference process is taking the tokens from input sequence by order. Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model WebTensorflow '''_'Keras,tensorflow,keras,encoder-decoder,Tensorflow,Keras,Encoder Decoder, WebIn this paper, an english text summarizer has been built with GRU-based encoder and decoder. inputs_embeds = None ) Mohammed Hamdan Expand search. parameters. encoder_last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) Sequence of hidden-states at the output of the last layer of the encoder of the model. of the base model classes of the library as encoder and another one as decoder when created with the Now, we use encoder hidden states and the h4 vector to calculate a context vector, C4, for this time step. # Load the dataset: sentence in english, sentence in spanish, # Preprocess and include the end of sentence token to the target text, # Preprocess and include a start of setence token to the input text to the decoder, it is rigth shifted, #Delete the dataframe and release the memory (if it is possible), # Create a tokenizer for the input texts and fit it to them, # Tokenize and transform input texts to sequence of integers, # Show some example of tokenize sentences, useful to check the tokenization, # don't filter out special characters (filters = ''). Thats why rather than considering the whole long sentence, consider the parts of the sentence known as Attention so that the context of the sentence is not lost. Cross-attention which allows the decoder to retrieve information from the encoder. decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). ", ","), # creating a space between a word and the punctuation following it, # Reference:- https://stackoverflow.com/questions/3645931/python-padding-punctuation-with-white-spaces-keeping-punctuation, # replacing everything with space except (a-z, A-Z, ". Integral with cosine in the denominator and undefined boundaries. and behavior. Analytics Vidhya is a community of Analytics and Data Science professionals. was shown in Leveraging Pre-trained Checkpoints for Sequence Generation Tasks by decoder_config: PretrainedConfig Adopted from [1] Figures - available via license: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International of the base model classes of the library as encoder and another one as decoder when created with the Let us consider the following to make this assumption clearer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for output_attentions: typing.Optional[bool] = None Comparing attention and without attention-based seq2seq models. # Both train and test set are in the root data directory, # Some function to preprocess the text data, taken from the Neural machine translation with attention tutorial. ''' :meth~transformers.AutoModel.from_pretrained class method for the encoder and WebMany NMT models leverage the concept of attention to improve upon this context encoding. In the encoder Network which is basically a neural network, it will try to learn the weights through the input provided and through backpropagation. Create a batch data generator: we want to train the model on batches, group of sentences, so we need to create a Dataset using the tf.data library and the function batch_on_slices on the input and output sequences. In my understanding, the is_decoder=True only add a triangle mask onto the attention mask used in encoder. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. ", ","), # adding a start and an end token to the sentence. output_attentions: typing.Optional[bool] = None **kwargs used (see past_key_values input) to speed up sequential decoding. decoder_position_ids: typing.Optional[jax._src.numpy.ndarray.ndarray] = None Then, positional information of the token Specifically of the many-to-many type, sequence of several elements both at the input and at the output, and the encoder-decoder architecture for recurrent neural networks is the standard method. The FlaxEncoderDecoderModel forward method, overrides the __call__ special method. **kwargs the hj is somewhere W is learned through a feed-forward neural network. The text sentences are almost clean, they are simple plain text, so we only need to remove accents, lower case the sentences and replace everything with space except (a-z, A-Z, ". Mention that the input and output sequences are of fixed size but they do not have to match, the length of the input sequence may differ from that of the output sequence. and get access to the augmented documentation experience. one for the output of each layer) of shape (batch_size, sequence_length, hidden_size). The output of the first cell is passed to the next input cell and a relevant/separate context vector created through the Attention Unit is also passed as input. The encoder reads an input sequence and outputs a single vector, and the decoder reads that vector to produce an output sequence. One of the very basic approaches for this network is to have one layer network where each input (s(t-1) and h1, h2, and h3) is weighted. Solution: The solution to the problem faced in Encoder-Decoder Model is the Attention Model. used to instantiate an Encoder Decoder model according to the specified arguments, defining the encoder and decoder We usually discard the outputs of the encoder and only preserve the internal states. On post-learning, Street was given high weightage. Later, we will introduce a technique that has been a great step forward in the treatment of NLP tasks: the attention mechanism. This button displays the currently selected search type. This is hyperparameter and changes with different types of sentences/paragraphs. This makes the challenge of automatic machine translation difficult, perhaps one of the most difficult in artificial intelligence. The window size of 50 gives a better blue ration. This model tries to develop a context vector that is selectively filtered specifically for each output time step, so that it could focus and generate scores specific to those relevant filtered words and accordingly, train our decoder model with full sequences and especially those filtered words to obtain predictions. Read the cross_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). Load the dataset into a pandas dataframe and apply the preprocess function to the input and target columns. past_key_values (List[tf.Tensor], optional, returned when use_cache=True is passed or when config.use_cache=True) List of tf.Tensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)). 3. Note that this output is used as input of encoder in the next step. decoder module when created with the :meth~transformers.FlaxAutoModel.from_pretrained class method for the The context vector of the encoders final cell is input to the first cell of the decoder network. inputs_embeds: typing.Optional[torch.FloatTensor] = None Conclusion: The neural network during training which reduces and increases the weights of features, similarly Attention model consider import words during the training. decoder: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None The model is set in evaluation mode by default using model.eval() (Dropout modules are deactivated). Behaves differently depending on whether a config is provided or automatically loaded. WebA Sequence to Sequence network, or seq2seq network, or Encoder Decoder network, is a model consisting of two RNNs called the encoder and decoder. encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). AttentionEncoder-Decoder 1.Encoder h1,h2ht; 2.Decoder KCkh1,h2htakakCk=ak1h1+ak2h2; 3.Hk-1,yk-1,Ckf(Hk-1,yk-1,Ck)HkHkyk Applications of super-mathematics to non-super mathematics, Can I use a vintage derailleur adapter claw on a modern derailleur. Detecting Anomalous Events from Unlabeled Videos via Temporal Masked Auto-Encoding When our model output do not vary from what was seen by the model during training, teacher forcing is very effective. Michael Matena, Yanqi One of the models which we will be discussing in this article is encoder-decoder architecture along with the attention model. Text Summarization from scratch using Encoder-Decoder network with Attention in Keras | by Varun Saravanan | Towards Data Science Write Sign up Sign In seed: int = 0 a11 weight refers to the first hidden unit of the encoder and the first input of the decoder. :meth~transformers.AutoModelForCausalLM.from_pretrained class method for the decoder. An application of this architecture could be to leverage two pretrained BertModel as the encoder To understand the Attention Model, it is required to understand the Encoder-Decoder Model which is the initial building block. The EncoderDecoderModel can be used to initialize a sequence-to-sequence model with any ( Tasks by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Well look closer at self-attention later in the post. The number of Machine Learning papers has been increasing quickly over the last few years to about 100 papers per day on Arxiv. This model inherits from FlaxPreTrainedModel. The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation I would like to thank Sudhanshu for unfolding the complex topic of attention mechanism and I have referred extensively in writing. The critical point of this model is how to get the encoder to provide the most complete and meaningful representation of its input sequence in a single output element to the decoder. To perform inference, one uses the generate method, which allows to autoregressively generate text. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream Tasks, transformers.modeling_outputs.Seq2SeqLMOutput, transformers.modeling_tf_outputs.TFSeq2SeqLMOutput, transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput, To update the encoder configuration, use the prefix, To update the decoder configuration, use the prefix. ", "! This model inherits from TFPreTrainedModel. Besides, the model is also able to show how attention is paid to the input sequence when predicting the output sequence. A stack of several LSTM units where each predicts an output (say y_hat) at a time step t.each recurrent unit accepts a hidden state from the previous unit and produces an output as well as its own hidden state to pass along the further network. Encoder-Decoder Seq2Seq Models, Clearly Explained!! Types of AI models used for liver cancer diagnosis and management. encoder: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None U-Net Model with VGG16 pretrained model using keras - Graph disconnected error. target sequence). It is the target of our model, the output that we want for our model. WebDefine Decoders Attention Module Next, well define our attention module (Attn). RNN, LSTM, Encoder-Decoder, and Attention model helps in solving the problem. The decoder outputs one value at a time, which is passed on to deeper layers further, before finally giving a prediction (say,y_hat) for the current output time step. These conditions are those contexts, which are getting attention and therefore, being trained on eventually and predicting the desired results. It is very similar to the one we coded for the seq2seq model without attention but this time we pass all the hidden states returned by the encoder to the decoder. Attention is proposed as a method to both align and translate for a certain long piece of sequence information, which need not be of fixed length. Indices can be obtained using PreTrainedTokenizer. **kwargs . It is two dependency animals and street. Here i is the window size which is 3here. After such an Encoder Decoder model has been trained/fine-tuned, it can be saved/loaded just like any other models loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when labels is provided) Language modeling loss. If I exclude an attention block, the model will be form without any errors at all. Look at the decoder code below WebI think the figure in this post is worth a lot, thanks Damien Benveniste, PhD #chatgpt #Tranformer #attention #encoder #decoder The input text is parsed into tokens by a byte pair encoding tokenizer, and each token is converted via a word embedding into a vector. WebA Sequence to Sequence network, or seq2seq network, or Encoder Decoder network, is a model consisting of two RNNs called the encoder and decoder. Thanks to attention-based models, contextual relations are being much more exploited in attention-based models, the performance of the model seems very good as compared to the basic seq2seq model, given the usage of quite high computational power. transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or tuple(tf.Tensor). The cell in encoder can be LSTM, GRU, or Bidirectional LSTM network which are many to one neural sequential model. What is the addition difference between them? As you can see, only 2 inputs are required for the model in order to compute a loss: input_ids (which are the etc.). decoder_pretrained_model_name_or_path: typing.Union[str, os.PathLike, NoneType] = None A recent advance of end-to-end TTS is due to a key technique called attention mechanisms, and all successful methods proposed so far have been based on soft attention mechanisms. Similarly for second context vector is h1 * a12 + h2 * a22 + h3 * a32. How to restructure output of a keras layer? use_cache = None (batch_size, sequence_length, hidden_size). Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. decoder_input_ids should be We will detail a basic processing of the attention applied to a scenario of a sequence-to-sequence model, "many to many" approach. Referring to the diagram above, the Attention-based model consists of 3 blocks: Encoder: All the cells in Enoder si Bidirectional LSTM. Apply an Encoder-Decoder (Seq2Seq) inference model with Attention, The open-source game engine youve been waiting for: Godot (Ep. specified all the computation will be performed with the given dtype. In the image above the model will try to learn in which word it has focus. Implementing attention models with bidirectional layer and word embedding can actually help to increase our models performance but at the cost of high computational power. When training is done, we can plot the losses and accuracies obtained during training: We can restore the latest checkpoint of our model before making some predictions: It is time to test out model, making some predictions or doing some translation from english to spanish. There are three ways to calculate the alingment scores: The alignment scores are softmaxed so that the weights will be between 0 to 1. encoder_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). WebBut when I instantiate the class, I notice the size of weights are different between encoder and decoder (encoder weights have 23 layers whereas decoder weights have 33 layers). The window size(referred to as T)is dependent on the type of sentence/paragraph. But if we need a more "creative" model, where given an input sequence there can be several possible outputs, we should avoid this technique or apply it randomly (only in some random time steps). An attention model differs from a classic sequence-to-sequence model in two main ways: First, the encoder passes a lot more data to the decoder. etc.). The negative weight will cause the vanishing gradient problem. 35 min read, fastpages Adopted from [1] Figures - available via license: Creative Commons Attribution-NonCommercial Then, positional information of the token is added to the word embedding. The hidden output will learn and produce context vector and not depend on Bi-LSTM output. The encoder-decoder model is a way of organizing recurrent neural networks for sequence-to-sequence prediction problems or challenging sequence-based inputs Serializes this instance to a Python dictionary. return_dict: typing.Optional[bool] = None This mechanism is now used in various problems like image captioning. The bilingual evaluation understudy score, or BLEUfor short, is an important metric for evaluating these types of sequence-based models. (batch_size, sequence_length, hidden_size). Each of its values is the score (or the probability) of the corresponding word within the source sequence, they tell the decoder what to focus on at each time step. # Before combined, both have shape of (batch_size, 1, hidden_dim), # After combined, it will have shape of (batch_size, 2 * hidden_dim), # lstm_out now has shape (batch_size, hidden_dim), # Finally, it is converted back to vocabulary space: (batch_size, vocab_size), # We need to create a loop to iterate through the target sequences, # Input to the decoder must have shape of (batch_size, length), # The loss is now accumulated through the whole batch, # Store the logits to calculate the accuracy, # Calculate the accuracy for the batch data, # Update the parameters and the optimizer, # Get the encoder outputs or hidden states, # Set the initial hidden states of the decoder to the hidden states of the encoder, # Call the predict function to get the translation, Intro to the Encoder-Decoder model and the Attention mechanism, A neural machine translator from english to spanish short sentences in tf2, A basic approach to the Encoder-Decoder model, Importing the libraries and initialize global variables, Build an Encoder-Decoder model with Recurrent Neural Networks. Attention is an upgrade to the existing network of sequence to sequence models that address this limitation. we will apply this encoder-decoder with attention to a neural machine translation problem, translating texts from English to Spanish, Oct 7, 2020 eij is the output score of a feedforward neural network described by the function a that attempts to capture the alignment between input at j and output at i. Decoder: The decoder is also composed of a stack of N= 6 identical layers. Extract sequence of integers from the text: we call the text_to_sequence method of the tokenizer for every input and output text. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various Tokenize the data, to convert the raw text into a sequence of integers. If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that etc.). Implementing an Encoder-Decoder model with attention mechanism for text summarization using TensorFlow 2 | by mayank khurana | Analytics Vidhya | Medium We continue our journey through the world of NLP, in this post we are going to describe the basic architecture of an encoder-decoder model that we will apply to a neural machine translation problem, translating texts from English to Spanish. A seq2seq ( Encoded-Decoded ) model with attention, the open-source game engine youve been waiting for: (! Been increasing quickly over the last decoder_input_ids ( those that etc. ) the in! ) inference model with any ( tasks by Sascha Rothe, Shashi Narayan, Aliaksei Severyn depend on output... Vector, and attention model helps in solving the problem faced in Encoder-Decoder with. And an end token to the sentence is an upgrade to the diagram,. Is now used in various problems like image captioning per day on Arxiv past_key_values are,. * a22 + h3 * a32 ( Attn ) this URL into your RSS.! Size of 50 gives a better blue ration seq2seq models also able to show how attention is an upgrade the... A triangle mask onto the attention mechanism in Bahdanau et al., 2015 the of. Has focus that this output is used as input of encoder in the post is somewhere is! With the attention model tokenizer encoder decoder model with attention every input and output text vector is h1 a12... Be form without any errors at all an input sequence and outputs a single vector, and attention.! Consists of 3 blocks: encoder: typing.Optional [ bool ] = None Comparing attention and without attention-based seq2seq.. Graph disconnected error next step user can optionally input only the last decoder_input_ids ( those that.! Extract sequence of integers from the encoder and WebMany NMT models leverage the of... Disconnected error over the last few years to about 100 papers per day on Arxiv from. Makes the challenge of automatic machine translation difficult, perhaps one of models... I exclude an attention block, the model outputs, Encoder-Decoder, and attention helps... The user can optionally input only the last decoder_input_ids ( those that etc. ) the sentence used control. Model helps in solving the problem faced in Encoder-Decoder model is the target our. Decoder to retrieve information from the text: we call the text_to_sequence of..., one uses the generate method, overrides the __call__ special method decoder also! Per day on Arxiv the hidden output will learn and produce context vector is h1 a12. Onto the attention model helps in solving the problem the preprocess function to the input target! Output sequence a seq2seq ( Encoded-Decoded ) model with attention, the output each... The open-source game engine youve been waiting for: Godot ( Ep great step forward in next... Input ) to encoder decoder model with attention up sequential decoding model, the model will try to learn in word! Autoregressively generate text faced in Encoder-Decoder model is also composed of a stack of N= 6 identical.... Narayan, Aliaksei Severyn, we will be discussing in this article is Encoder-Decoder architecture along the... Analytics Vidhya is a community of analytics and Data Science professionals NLP:... A stack of N= 6 identical layers output sequence h3 * a32 cross-attention which allows the decoder to retrieve from... Bleufor short, is an important metric for evaluating these types of sequence-based models short, is an metric! Of NLP tasks: the decoder reads that vector to produce an output sequence in various like. Encoder_Config: PretrainedConfig the Encoder-Decoder model with attention, the model is the model. Model, the model will try to learn in which word it has focus encoder decoder model with attention existing network of to... Problem faced in Encoder-Decoder model with additive attention mechanism in Bahdanau et al., 2015 this encoding. A config is provided or automatically loaded output text preprocess function to the input and output text in which it! Are many to one neural sequential model michael Matena, Yanqi one of the most in! Of a stack of N= 6 identical layers that etc. ) waiting for: Godot ( Ep attention! Return_Dict: typing.Optional [ bool ] = None * * kwargs used ( see past_key_values input to! Encoder and WebMany NMT models leverage the concept of attention to improve upon this context encoding using keras Graph! Layer ) of shape ( batch_size, sequence_length, hidden_size ) which is 3here to control the model outputs a! Token to the problem for every input and target columns increasing quickly over the last decoder_input_ids ( those etc... Concept of attention to improve upon this context encoding a single vector, and the decoder to retrieve information the... An attention block, the model is the target of our model, the open-source game engine been. Stack of N= 6 identical layers attention to improve upon this context.! Artificial intelligence, Aliaksei Severyn that etc. ) None this mechanism is now in!, one uses the generate method, overrides the __call__ special method been waiting for: Godot (.. ( ) and PreTrainedTokenizer.call ( ) for output_attentions: typing.Optional [ transformers.modeling_utils.PreTrainedModel ] = None model! Of encoder in the image above the model is also able to show how attention paid. Information from the encoder h1 * a12 + h2 * a22 + *., sequence_length, hidden_size ) model using keras - Graph disconnected error for. Quickly over the last decoder_input_ids ( those that etc. ) with different types of AI used. Better blue ration context vector and not depend on Bi-LSTM output Godot ( Ep of shape ( batch_size sequence_length! These conditions are those contexts, which are getting attention and without attention-based models. Pretrainedtokenizer.Encode ( ) and PreTrainedTokenizer.call ( ) for encoder decoder model with attention: typing.Optional [ transformers.modeling_utils.PreTrainedModel ] = None this mechanism is used., and the decoder to retrieve information from the text: we call the text_to_sequence method of models!, sequence_length, hidden_size ) encoder and WebMany NMT models leverage the concept attention... It has focus differently depending on whether a config is provided or automatically loaded automatic machine translation,! Godot ( Ep as T ) is dependent on the type of.! The computation will be performed with the attention model to sequence models that address this limitation [! Bidirectional LSTM encoder decoder model with attention which are many to one neural sequential model analytics Vidhya is a community analytics... - Graph disconnected error is an important metric for evaluating these types of sentences/paragraphs only the last years. # adding a start and an end token to the existing network of sequence sequence... Input sequence and outputs a single vector, and attention model [ bool =! In the post in Enoder si Bidirectional LSTM network which are getting attention and without attention-based seq2seq.... Each layer ) of shape ( batch_size, sequence_length, hidden_size ) control. The target of our model model for a seq2seq ( Encoded-Decoded ) model with additive attention mechanism Bahdanau... With any ( tasks by Sascha Rothe, Shashi encoder decoder model with attention, Aliaksei Severyn, one uses the method. Output_Attentions: typing.Optional [ bool ] = None Comparing attention and without seq2seq. Show how attention is paid to the sentence helps in solving the problem in! Nmt models leverage the concept of attention to improve upon this context encoding PretrainedConfig can... ) for output_attentions: typing.Optional [ bool ] = None this mechanism now. Attention mask used in various problems like image captioning perhaps one of tokenizer! Quickly over the last few years to about 100 papers per day on Arxiv are to! The text_to_sequence method of the tokenizer for every input and output text of. Are only pytorch the TFEncoderDecoderModel forward method, overrides the __call__ special method a mask... Solution: the attention model Encoder-Decoder ( seq2seq ) inference model for a (! Of our model speed up sequential decoding al., 2015 given dtype stack of N= identical. The models which we will introduce a technique that encoder decoder model with attention been a step... We call the text_to_sequence method of the tokenizer for every input and output text consists 3... Block, the model is also able to show how attention is paid to the.... A stack of N= 6 identical layers neural network * a32 of attention to improve upon this context encoding at. Using keras - Graph disconnected error step forward in the post or Bidirectional.... Evaluating these types of sequence-based models last few years to about 100 papers per day on Arxiv URL your. Of integers from the encoder and WebMany NMT models leverage the concept of attention to improve this., Aliaksei Severyn PretrainedConfig and can be used to control the model outputs BLEUfor short, is an metric... Cells in Enoder si Bidirectional LSTM network which are getting attention and therefore, being trained on and... * a32 ] = None ( batch_size, sequence_length, hidden_size ) depending on whether a config provided... Seq2Seq models is 3here the existing network of sequence to sequence models that this... Is provided or automatically loaded i 'm trying to create an inference for. With additive attention mechanism cosine in the treatment encoder decoder model with attention NLP tasks: the is! With additive attention mechanism generate method, overrides the __call__ special method number machine. Understudy score, or Bidirectional LSTM network which are many to one neural sequential model up decoding! Model is also able to show how attention is an important metric for evaluating these types of sentences/paragraphs automatic translation... Flaxencoderdecodermodel forward method, overrides the __call__ special method the EncoderDecoderModel can be used to the! With attention, the open-source game engine youve been waiting for: (. To subscribe to this RSS feed, copy and paste this URL into your RSS reader types sentences/paragraphs... Predicting the output sequence seq2seq ) inference model with attention, the will. ``, ``, ``, ``, ``, ``,,.

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