Symanto Brain is based on zero- and few-shot technologies in accordance with the latest neural language models. Specifically, it uses semantic matching methods from a neural network with more than 300 million parameters and dozens of neural layers (deep neural network), trained with texts in more than 50 languages and fine-tuned for natural language inference, paraphrasing and other series of classification tasks (e.g., feeling, emotions, topic extraction, personality traits, etc.) in all types of data sources (e.g., social networks, reviews, news, etc.).
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Classes or also called labels, between which to discriminate (classify) the text, e.g.
Status colour Red title Negative Status colour Green title positive Patterns, also known as label descriptors, allow the semantic matching between the analysed text and the different labels, e.g.
This text is {}
Input: The product has no issues but the packaging causes so much extra to squirt out and you can't stop it. For how expensive it is it's such a waste.
Result:
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With this, the zero-shot model allows rapid classification of the data set without the need for prior training and with results similar to those of a deep learning model trained with large amounts of data, whereas the few-shot model allows one to adjust the quality to a high extent by providing only a few annotated texts instead taking hours to annotate big datasets.
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