How does Symanto Brain work

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.).

Key Elements

To create any model using Symanto Brain, there are two key elements required: 

• Classes or also called labels, between which to discriminate (classify) the text, e.g. Negative positive

• Patterns, also known as label descriptors, allow the semantic matching between the analysed text and the different labels, e.g. This text is {}

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.

How it works

Specifically, Symanto Brain works as follows: 

• With zero-shot, the user can create their 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 user can train their 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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