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In the process of encoding, the sender (i.e. encoder) uses verbal (e.g. words, signs, images, video) and non-verbal (e.g. body language, hand gestures, face expressions) symbols for which he or she believes the receiver (that is, the decoder) will understand. The symbols can be words and numbers, images, face expressions, signals and/or actions.
The term encoding-decoding model is used for any model that includes the phases of encoding and decoding in its description of communication. Such models stress that to send information, a code is necessary. A code is a sign system used to express ideas and interpret messages. Encoding-decoding models are sometimes contrasted with inferential ...
Schramm's model of communication was published by Wilbur Schramm in 1954. It is one of the earliest interaction models of communication. [1] [2] [3] It was conceived as a response to and an improvement over earlier attempts in the form of linear transmission models, like the Shannon–Weaver model and Lasswell's model.
In this regard, Berlo speaks of the source-encoder and the decoder-receiver. Treating the additional components separately is especially relevant for technical forms of communication. For example, in the case of a telephone conversation, the message is transmitted as an electrical signal and the telephone devices act as encoder and decoder.
In this example, you (the decoder) have something in common with the Canadian company that produced the commercial (the encoder), which allows you to share the same logic used by the Canadian company. When the receiver/decoder interprets the sign using the same logic as the encoder, it can be called a “preferred reading” (Meagher 185). [6]
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.
Encoder self-attention, block diagram Encoder self-attention, detailed diagram. Self-attention is essentially the same as cross-attention, except that query, key, and value vectors all come from the same model. Both encoder and decoder can use self-attention, but with subtle differences.
The encoder transforms words into fuzzy sets (FSs) and leads to a codebook – words with their associated FS models. The outputs of the encoder activate a Computing With Words [ 1 ] (CWW) engine, whose output is one or more other FSs, which are then mapped by the decoder into a recommendation (subjective judgment) with supporting data.