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NLP entities extraction library using a pre-trained model

Experience this embeddable library by seeing how it works, learning more about how it can be used, and then deploying it with your application.

Please go through the following steps.

Step 1. Discover potential use cases

Read how entities extraction aids in achieving business goals.

Step 2. Try the interactive demo application

See how entities extraction works using an interactive demo application.

Step 3. Get an application code sample

Get a sample application code for Entity Extraction of a given text to identify PII data using a remote container with NLP libraries, runtime, and pre-trained models. The code is a complete app that points to the remote container. Follow the instructions inside the directory to run the application. Modify the code for experimentation. An additional code sample with modifications is also included. Assumes Python3+ is installed or download from https://www.python.org/downloads/. Refer to the readme.md file for instructions on different OS and shells.

  • Follow these steps, using the default container URL referenced in the application code repository, or refer to the readme.md. Please regularly check the repository to ensure you are using the latest URL. Note that the default URL doesn't allow for customization requests. For a URL supporting customization and advance change notification, please move forward to Step 3.2.

    1. python3 -m venv client-env
    2. source client-env/bin/activate
    3. pip3 install -r requirements.txt
    4. python3 <.py file name>
    5. From your browser access the local application at localhost:8050

  • Please login with your IBMid to access a new container URL, which can be used to replace the default URL in the application code. This updated URL provides enhanced stability for creating demos with advance notifications of change. In addition, you can make limited container customization requests, such as additional language models, which we will try to accommodate where possible. Please use "Contact Us" for getting in touch.

Step 4. Start experimenting in our sandbox environment

Explore our NLP library, pre-trained models and notebooks in a custom-built TechZone sandbox environment, courtesy of Watson Studio.

  • IBM Watson Studio provides cloud-based environment that simplifies the process of building, training and deploying machine learning and AI applications. It offers tools and resources for data handling, model creation and collaboration among team members.

    IBM TechZone hosts a collection of technical demos, POCs, prototypes and technical environments that can be accessed to experience IBM Technology. IBM Techzone access would be needed in order to reserve the Watson Studio environment to try the sentiment analysis model. In order to gain access to IBM Techzone, you must be a registered Partner Plus member. Signing up for a Partner Plus account is free and can be done using this link: https://ibm.biz/dsce-partnerplus

  • You can use your own Watson Studio environment on IBM Cloud or you can provision a temporary one for 72 hours on TechZone.

  • Read instructions in the following document to setup your sandbox environment before going through a tutorial.

  • Learn how to work with a data set and a pre-trained model that is not trained on the specific data set within the sandbox environment.

Step 5. Learn more

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