The decoder reverses the process, turning the vector into an output item, using the previous output as the input context. The encoder turns each item into a corresponding hidden vector containing the item and its context. The primary components are one encoder and one decoder network.
The context for each item is the output from the previous step. It does so by use of a recurrent neural network (RNN) or more often LSTM or GRU to avoid the problem of vanishing gradient. Seq2seq turns one sequence into another sequence ( sequence transformation). AlexaTM 20B achieved state-of-the-art performance in few-shot-learning tasks across all Flores-101 language pairs, outperforming GPT-3 on several tasks. It allows adding features across different languages without massive training workflows. Training mixes denoising (appropriately inserting missing text in strings) and causal-language-modeling (meaningfully extending an input text). The encoder outputs a representation of the input that the decoder uses as input to perform a specific task, such as translating the input into another language.The model outperforms the much larger GPT-3 in language translation and summarization. It uses an encoder-decoder to accomplish few-shot learning. In 2022, Amazon introduced AlexaTM 20B, a moderate-sized (20 billion parameter) seq2seq language model. Google claimed that the chatbot has 1.7 times greater model capacity than OpenAI's GPT-2, whose May 2020 successor, the 175 billion parameter GPT-3, trained on a "45TB dataset of plaintext words (45,000 GB) that was. In 2020, Google released Meena, a 2.6 billion parameter seq2seq-based chatbot trained on a 341 GB data set. An LSTM neural network then applies its standard pattern recognition facilities to process the tree. First, the equation is parsed into a tree structure to avoid notational idiosyncrasies. The company claimed that it could solve complex equations more rapidly and with greater accuracy than commercial solutions such as Mathematica, MATLAB and Maple. In 2019, Facebook announced its use in symbolic integration and resolution of differential equations. It has close links to other ontology projects such as the RNAO consortium, and the Biosapiens polypeptide features.The algorithm was developed by Google for use in machine translation. To provide a structured controlled vocabulary for the description of mutations at both the sequence and more gross level in the context of genomic databases.Were genes within model organism databases to be annotated with these terms then it would be possible to query all these databases for, for example, all genes whose transcripts are edited, or trans-spliced, or are bound by a particular protein. To provide for a structured representation of these annotations within databases.the annotations shared by a DAS server ( BioDAS, Biosapiens DAS), or annotations encoded by GFF3. To provide for a structured controlled vocabulary for the description of primary annotations of nucleic acid sequence, e.g.The Sequence Ontologies are provided as a resource to the biological community. SO also provides a rich set of attributes to describe these features such as “polycistronic” and “maternally imprinted”. There are also experimental features which are the result of an experiment. Biomaterial features are those which are intended for use in an experiment such as aptamer and PCR_product.
Biological features are those which are defined by their disposition to be involved in a biological process. SO includes different kinds of features which can be located on the sequence. The Sequence Ontology is a set of terms and relationships used to describe the features and attributes of biological sequence. For new term suggestions, please use the Term Tracker. For questions, please send mail to the SO developers mailing list. Our aim is to develop an ontology suitable for describing the features of biological sequences. SO is also part of the Open Biomedical Ontologies library.
Input to SO is welcomed from the sequence annotation community. Contributors to SO include the GMOD community, model organism database groups such as WormBase, FlyBase, Mouse Genome Informatics group, and institutes such as the Sanger Institute and the EBI. SO was initially developed by the Gene Ontology Consortium. SO is a collaborative ontology project for the definition of sequence features used in biological sequence annotation. This is the home page of the Sequence Ontology (SO). Welcome To SO Welcome to the Sequence Ontology
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