Named entity recognition (NER), also referred to as entity chunking, identification or extraction, is the task of detecting and classifying key information (entities) in text.
For example, in a sentence: Mary lives in Santa Clara and works at NVIDIA
, we should detect that Mary
is a person, Santa Clara
is a location and NVIDIA
is a company.
The best place to get started with TLT - NER would be the TLT - NER jupyter notebooks enclosed with this sample. This resource has two notebooks included.
.ejrvs
file.ejrvs
file and deploy it to Jarvis.If you are a seasoned Conversation AI developer we recommend installing TLT and referring to the TLT documentation for usage information.
Please make sure to install the following before proceeding further:
Note: A compatible NVIDIA GPU would be required.
We recommend that you install TLT inside a virtual environment. The steps to do the same are as follows
virtualenv -p python3 <name of venv>
source <name of venv>/bin/activate
pip install jupyter notebook # If you need to run the notebooks
TLT is python package that is hosted in nvidia python package index. You may install by using python’s package manager, pip.
pip install nvidia-pyindex
pip install nvidia-tlt
To download the jupyter notebook please:
ngc registry resource download-version "nvidia/tlt-jarvis/tokenclassification_notebook:v1.0"
jupyter notebook --ip 0.0.0.0 --allow-root --port 8888
By downloading and using the models and resources packaged with TLT Conversational AI, you would be accepting the terms of the Jarvis license