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    John Snow Labs Announces the NLP Lab: Free No-Code AI for Domain Experts

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    Ph.D. in Computer Science – Head of Product

    Over the last year, Annotation Lab has grown to be much more than a document annotation tool. It became a full-fledged AI system, capable of testing pre-trained models and rules, applying them to new datasets, training, and tuning models, and exporting them to be deployed in production. All those features together with the new Playground concept presented in the current release notes contributed to the transformation of the John Snow Labs Annotation Lab into the NLP Lab. A new Playground feature is released as part of the NLP Lab’s Hub that allows users to quickly test any model and/or rule on a snippet of text without the need to create a project and import tasks. NLP Lab also supports the training of Legal and Finance models and Model evaluation for classification projects. As always, the release includes some stabilization and bug fixes for issues reported by our user community. Below are the details of what has been included in this release.

    The Playground: Test, Demo, and Serve Models and Rules in NLP Lab

    NLP Lab introduces the Playground feature where users can directly deploy and test models and/or rules. In previous versions, the pre-annotation servers could only be deployed from within a given project. With the addition of the Playground, models can easily be deployed and tested on a sample text without going through the project setup wizard. Any model or rule can now be selected and deployed for testing by clicking on the “Open in Playground” button.

    NLP Lab Playground

    For each model, the Playground shows the benchmarking information where available. Benchmarking information are available for pretrained Heathcare, Finance and Legal models as well as for models trained within the NLP Lab.

    Rules are deployable in the Playground from the Rules page. When a particular rule is deployed to the Playground, the user can also change the definition of the rules via the form available on the right side of the page. After saving the changes,  users need to click on the “Deploy” button to refresh the results of the pre-annotation on the provided text.

    NLP Lab Model Testing

    Deployment of models and rules is supported by floating and air-gapped licenses. Healthcare, Legal, and Finance models require a license with their respective scopes to be deployed in Playground. Unlike pre-annotation servers, only one Playground instance can be deployed at any given time.

    Training and Preannotation with Finance and Legal Models

    With this release, users can perform training of Legal and Finance models depending on the available license(s). When training a new model in the NLP Lab, users have the option to select what library to use. Two options were available up until now: Open source and Healthcare. This release adds two new no-code NLP options: Legal and Finance. This helps differentiate the library used for training the models. The new options are only available when at least one valid license with the corresponding scope is added to the License page.

    Training and Active Learning

    Publish Trained Models to S3

    The NLP Lab also allows users to easily export trained models to a given s3 bucket. This feature is available on the Models page under the Hub tab. Users need to enter the s3 bucket path, s3 access key, and s3 secret key to upload the model to the s3 bucket.

    Training and Active Learning

    Getting Started is Easy

    The NLP Lab is a free tool that can be deployed in a couple of clicks on the AWS and Azure Marketplaces, or installed on-premise with a one-line Kubernetes script. Get started here: https://nlp.johnsnowlabs.com/docs/en/alab/install

    Get Started with NLP Lab

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    Ph.D. in Computer Science – Head of Product
    Our additional expert:
    Dia Trambitas is a computer scientist with a rich background in Natural Language Processing. She has a Ph.D. in Semantic Web from the University of Grenoble, France, where she worked on ways of describing spatial and temporal data using OWL ontologies and reasoning based on semantic annotations. She then changed her interest to text processing and data extraction from unstructured documents, a subject she has been working on for the last 10 years. She has a rich experience working with different annotation tools and leading document classification and NER extraction projects in verticals such as Finance, Investment, Banking, and Healthcare.

    The NLP Lab: Free No-Code AI by John Snow Labs

    Empowering Domain Experts to Build State-of-the-Art AI Solutions Without Code. Two years ago, Annotation Lab 1.0, no code AI platform, was launched...
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