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.
.rivafile and deploy it to Riva.
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 source /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-riva/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 Riva license