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T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [ 1 ] [ 2 ] Like the original Transformer model, [ 3 ] T5 models are encoder-decoder Transformers , where the encoder processes the input text, and the decoder generates the output text.
This initial version already contained Direct Speech Recognition and Direct Text To Speech APIs which applications could use to directly control engines, as well as simplified 'higher-level' Voice Command and Voice Talk APIs.
Seq2seq RNN encoder-decoder with attention mechanism, training Seq2seq RNN encoder-decoder with attention mechanism, training and inferring The attention mechanism is an enhancement introduced by Bahdanau et al. in 2014 to address limitations in the basic Seq2Seq architecture where a longer input sequence results in the hidden state output of ...
The decoder is a standard Transformer decoder. It has the same width and Transformer blocks as the encoder. It uses learned positional embeddings and tied input-output token representations (using the same weight matrix for both the input and output embeddings). It uses a byte-pair encoding tokenizer, of the same kind as used in GPT-2. English ...
FAAD2 – open-source decoder for Advanced Audio Coding. There is also FAAC, the same project's encoder, but it is proprietary (but still free of charge). libgsm – Lossy compression ; opencore-amr – Lossy compression (AMR and AMR-WB) liba52 – a free ATSC A/52 stream decoder (AC-3) libdca – a free DTS Coherent Acoustics decoder
Voyant "was conceived to enhance reading through lightweight text analytics such as word frequency lists, frequency distribution plots, and KWIC displays." [3] Its interface is composed of panels which perform these varied analytical tasks. These panels can also be embedded in external web texts (e.g. a web article could include a Voyant panel ...
As with all binary-to-text encoding schemes, Base64 is designed to carry data stored in binary formats across channels that only reliably support text content. Base64 is particularly prevalent on the World Wide Web [1] where one of its uses is the ability to embed image files or other binary assets inside textual assets such as HTML and CSS ...
Unlike previous models, BERT is a deeply bidirectional, unsupervised language representation, pre-trained using only a plain text corpus. Context-free models such as word2vec or GloVe generate a single word embedding representation for each word in the vocabulary, whereas BERT takes into account the context for each occurrence of a given word ...