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Can BERT be used for Seq2Seq?
Here are 3 of the coolest: Seq2Seq (Sequence to Sequence Translation)— uses an encoder-decoder architecture to translate between languages. BERT —this bi-directional encoder produced SOTA results in answering questions and filling in the blanks. Token masking and bi-directionality allows for exceptional context.
Is BERT encoder or decoder?
The NMT system in this paper is an encoder- decoder based on the Transformer. The out- puts from the BERT encoder, which are represen- tations of source words, are input to the context attention mechanism in the Transformer decoder to generate a translation.
How is BERT different from Transformer?
Introduction to BERT One of the difference is BERT use bidirectional transformer (both left-to-right and right-to-left direction) rather than dictional transformer (left-to-right direction). On the other hand, both ELMo use bidirectional language model to learn the text representations.
Does BERT use a decoder?
Ah, but you see, BERT does not include a Transformer decoder. It is only the encoder part, with a classifier added on top. For masked word prediction, the classifier acts as a decoder of sorts, trying to reconstruct the true identities of the masked words.
How to create a marianmt model in seq2seq?
All models are transformer encoder-decoders with 6 layers in each component. Each model’s performance is documented in a model card. The 80 opus models that require BPE preprocessing are not supported. To create a MarianMT translation model, you must provide the two parameters below. encoder_decoder_type: This should be “marian”.
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.
How many languages can seq2seq model be trained in?
The list of supported language pairs can be found here. The 1,000+ models were originally trained by Jörg Tiedemann using the Marian C++ library, which supports fast training and translation.
Which is a hugging face model in seq2seq?
This may be a Hugging Face Transformers compatible pre-trained model, a community model, or the path to a directory containing model files. This is required when using a custom knowledge_dataset. Seq2SeqModel has the following task-specific configuration options. Name of the base Marian model used to load the tokenizer.