Sequence To Sequence Model
A sequence-to-sequence (seq2seq) model is a class of neural network models designed to transform an input sequence into an output sequence, where the two sequences can have different lengths, commonly implemented using an encoder-decoder architecture.
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Glossary Terms(6)
Beam Search
Beam search is a heuristic decoding algorithm that generates text by tracking the top-scoring k partial sequences (the "beam width") at each step, expanding ea…
BLEU Score
BLEU (Bilingual Evaluation Understudy) is a precision-based metric that scores machine-generated text, most commonly machine translation output, by comparing o…
ROUGE Score
ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a set of recall-focused metrics for evaluating automatic text summarization by measuring the overl…
Transformer Architecture
The Transformer is a neural network architecture built entirely around the self-attention mechanism, allowing it to model relationships between all elements of…
Encoder-Decoder Model
An encoder-decoder model is a neural network architecture split into two components — an encoder that processes an input sequence into an internal representati…
Sequence-to-Sequence Model
A sequence-to-sequence (seq2seq) model is a class of neural network models designed to transform an input sequence into an output sequence, where the two seque…