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TLT/Jarvis - Named Entity Recognition

TLT/Jarvis - Named Entity Recognition

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Description
This collection contains models and notebooks for Token Classification training and deployment with TLT and Jarvis respectively
Curator
NVIDIA
Modified
March 14, 2025
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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.