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TAO/Riva - Text Classification

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Description

This collection contains models and notebooks for Text Classification training and deployment with TAO Toolkit and Riva respectively.

Curator

NVIDIA

Modified

August 25, 2021
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Text Classification

Overview

This page contains the information about the Text Classification collection with TAO. Text classification models can be used for text classification problems such as sentiment analysis or domain/intent detection for dialogue systems. A text sequence is given to such models as input and the models predict a label for it.

These models are usually data specific and will recognize specific text categories or query domains that were presented in a training dataset.

Model Architecture

All the models have a simple and very effective architecture based on BERT-like models. Text classification models consists of two main modules:

  • A encoder module which is a pre-trained BERT-like models such as BERT, RoBEERTa or Megatron.
  • A decoder module which is an MLP classifier on the output of the first token [CLS].

Available Models

For instructions on how to use a model, please see its corresponding model card page:

License

License to use these models is covered by the Model EULA. By downloading the model checkpoints, you accept the terms and conditions of these licenses.

Suggested Reading

Ethical AI

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.