Token Classification Collection

This page contains the information about the Token Classification collection with TLT.

NGC Model Collection: Token Classification ==========================================

Overview --------

This collection contains end-to-end neural models for Token Classification Tasks using the Transfer Learning Toolkit (TLT). The TokenClassification Model in TLT supports Named entity recognition (NER), part-of-speech tagging and other token level classification tasks.

Available Models ----------------

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

• Named entity recognition model with Bidirectional Encoder Representations from Transformers (BERT)

References ----------

• Devlin, Jacob, et al. "Bert: Pre-training of deep bidirectional transformers for language understanding." arXiv preprint arXiv:1810.04805 (2018).

Suggested Reading -----------------

• More information about the Transfer Learning Toolkit can be found at the NVIDIA Developer Zone: https://developer.nvidia.com/transfer-learning-toolkit
• Read the TLT getting Started guide and release notes.
• More information about the experiment spec files can be found in the TLT User Guide

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.