...
A deep learning model is a trained model using a neural network architecture or a set of labelled data that contains multiple layers.
FASL
...
FASL is the combination of Few-shot learning, which addresses the problem of learning new, unseen concepts quickly with a limited number of annotated training samples and Active learning, which is based on the idea that smart sampling of data leads to faster training and more accurate modelsF1 score
An evaluation metric that measures a model’s accuracy. It combines the precision and recall scores of a model. The accuracy metric computes how many times a model made a correct prediction across the entire dataset. This can be a reliable metric only if the dataset is class-balanced; that is, each class of the dataset has the same number of samples.
...
Few-shot learning
Few-shot learning refers to the model’s ability toclassify new data when only a limited number of training instances (e.g., 10 to 100) have been provided. As a result, after being exposed to a small amount of prior information, the model improves its performance.
...
A language model based on neural networks , exploiting exploits their ability to learn distributed representations to reduce the need for huge numbers of training examples when learning highly complex functions.
...
Pattern
Patters Patterns are descriptive sentences which assist the model in understanding and better detecting your label.
...
Prompt Ranker
A tool for unsupervised prompt ranking. Prompts (or hypothesis, or label descriptions), are the key components that make Symanto Brain work. Without annotated data (which is often the case when using Symanto Brain), finding a good prompt is hard: PromptRanker helps you do that. Upload a dataset to be classified and manually write a list of different prompts for each label in the label space: PromptRanker will return a number for each prompt to score the prompts according to their estimated trustworthiness. The higher the score, the better the prompt.
...
Semantic matching
Semantic matching is a technique to determine whether two or more elements have a similar meaning.
...
Text Classification is the process of categorizing text into one or more different classes to organize, structure, and filter into any parameter.
...
Zero-shot learning
Zero-shot learning refers to the model’s ability to classify objects that it has never seen before, or in other words, allow allows us to assign an appropriate label to a piece of text without having received any training examples before.
...