Search Results for "labels"

...Nowadays, the dominant approach to build such classifiers is machine learning, that is learning classification rules from examples. In order to build such classifiers, we need labeled data, which consists of documents and their corresponding categories (or tags, or labels).

...Instead of focusing on one task, StarSpace supports multi-task and multi-domain setups—for instance, you can train embeddings so that textual queries match item descriptions, sentences map to labels, or users align with liked items in the same embedding space. The training objective is contrastive: for a given query embedding, positive and negative examples are sampled and the model is optimized to score positive higher than negatives. The library supports a variety of tasks (text classification, nearest-neighbor search, recommendation, entity linking) with simple configuration. ...

AFNER is a C++ named entity recognition system that uses machine learning techniques. It is customisable to various domains. It also allows for multiple and overlapping named entity labels.

BCAR is a library for the associative classification, which denotes "Boosting Class Association Rules". BCAR provides a general tool for classification tasks with various types of input data.