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 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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Specifically, Symanto Brain works as follows:
With zero-shot, the researcher can
create his own model only by configuring the task to approximate (defining patterns and labels) and expose the API for consumption. In this case, there is a single step, the use of the model based on requests to the exposed API.With few-shot, the researcher can train his own model by providing it with some annotated data and exposes the API for consumption. In this case, there are two steps, 1) in model training; 2) the use of the exposed model.
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