Tasks

This section gives you ideas about the kind of tasks you can use Rubrix for. It also describes some of the tasks on our roadmap, if there’s some task you want and don’t see here or you want to contribute a task, file an issue or use the Discussion forum at Rubrix’s GitHub page.

Supported tasks

Text classification

According to the amazing NLP Progress resource by Seb Ruder:

Text classification is the task of assigning a sentence or document an appropriate category. The categories depend on the chosen dataset and can range from topics.

Rubrix is flexible with input and output shapes, which means you can model many related tasks like for example:

Key phrase extraction

Token classification

The most well-known task in this category is probably Named Entity Recognition:

Named entity recognition (NER) is the task of tagging entities in text with their corresponding type. Approaches typically use BIO notation, which differentiates the beginning (B) and the inside (I) of entities. O is used for non-entity tokens.

Rubrix is flexible with input and output shapes, which means you can model related tasks like for example:

Tasks on the roadmap

Natural language processing

  • Text2Text, covering summarization, machine translation, natural language generation, etc.

  • Question answering

Computer vision

  • Image classification

  • Image captioning

Speech

  • Speech2Text