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What is a Seq2Seq model?
Jul 13, 2019·6 min read. Sequence-to-sequence (abrv. Seq2Seq) models are deep learning models that have achieved a lot of success in tasks like machine translation, text summarization, and image captioning. Google Translate started using such a model in production in late 2016.
Is Seq2Seq a language model?
Seq2seq is a family of machine learning approaches used for language processing. Applications include language translation, image captioning, conversational models and text summarization.
How do you determine the number of LSTM units?
The selection of the number of hidden layers and the number of memory cells in LSTM probably depends on the application domain and context where you want to apply this LSTM. The optimal number of hidden units could be smaller than the number of inputs. AFAIK, there is no rule like multiply the number of inputs with N.
Where did the idea of seq2seq come from?
Ideation of Seq2Seq or sequence-to-sequence models came in a paper by Ilya Sutskever et.al. in “ Sequence to Sequence Learningwith Neural Networks ”. They are essentially a certain organization of deep sequential models (a.k.a. RNN based models) (e.g. LSTMs/GRUs) [ 1] (discussed later).
What does none mean in seq2seq model?
The first None means the batch size, and the batch size is unknown since user can set it. The second None means the lengths of sentences. The maximum length of setence is different from batch to batch, so it cannot be set with the exact number.
What kind of transformers are used in seq2seq?
The Seq2SeqModel class is used for Sequence-to-Sequence tasks. Currently, four main types of Sequence-to-Sequence models are available. The decoder must be a bert model. The encoder can be one of [bert, roberta, distilbert, camembert, electra]. The encoder and the decoder must be of the same “size”.
How to create a generic encoder-decoder model with seq2seq?
To create a generic Encoder-Decoder model with Seq2SeqModel, you must provide the three parameters below. encoder_type: The type of model to use as the encoder. encoder_name: The exact architecture and trained weights to use.