NLP Lab
4.4
1

Not Claimed

NLP Lab is a no-code platform for document labeling and AI/ML model training that allows domain experts to extract meaningful facts from text documents, images or PDFs and train models that will automatically predict those facts on new documents. It uses Spark NLP and Spark OCR pre-trained models or can tune models to better handle specific use cases. It supports the end-to-end process from starting an annotation project to the deployment of a trained model, all without writing a line of code. It has an auto-scaling architecture powered by Kubernetes and provides enterprise-grade security for free, including support for air-gap environments, zero data sharing, role-based access, full audit trails, MFA, and identity provider integrations. It allows powerful experiments for model training and finetuning, model testing, and model deployment as API endpoints.
Developer
John Snow Labs
HQ Location
Lewes, Delaware
Strengths
  • Accuracy

    Highly accurate natural language processing

  • Ease of use

    User-friendly interface and easy to navigate

  • Customization

    Ability to customize and train models for specific use cases

Weaknesses
  • Pricing

    Relatively expensive compared to other NLP tools

  • Limited languages

    Supports only a limited number of languages

  • Integration

    Limited integration options with other software

Opportunities
  • Opportunity to expand language support and integrations
  • Potential to add new features and capabilities
  • Opportunity to form partnerships with other software providers
Threats
  • Competition from other established NLP tools
  • Rapidly evolving technology may make current features obsolete
  • Potential impact of economic downturn on customer demand

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NLP Lab Plan

NLP Lab offers a free version with limited features and a paid version for $99/month with full access to all features.
Community $ Free
NLP Lab is a Free tool, with support for unlimited projects, users, documents, community models, teams, analytics, workflows, audit, No-Code Model Training & Active Learning. Support for text, image, video and audio annotations Multi-lingual text labeling Span annotations & overlapping entities Label relations & dependencies Unlimited projects, users, documents, models Project grouping Project searching/filtering Full API access Task assignment Task tagging and comments Role based task status Duplicate tasks detection Feedback & Comments on completions Consensus Analysis/IA Agreement Quality review workflows Performance Dashboards Project configuration based on existing models Pre-annotations with model assisted labeling Active learning: auto-retrain models on the fly Transfer learning: start from pre-trained models Bring your own models Precision/Recall Reports for Trained Models Model Hub Integration Role based access control Annotation Versioning Full audit trail LDAP/AD integration - User import/Login On-Premise / Air-Gap Deployment
Healthcare $ Free Trial 1 License Per Year
The Healthcare edition offers access to pretrained healthcare-specific models published on the NLP Models Hub. All features from the Community library Pre-annotations with rules & regex Automatic detection of 400+ clinical and biomedical entities Automatic detection of clinical assertion status detection (negation detection) Automatic detection of 60+ healthcare-specific relation extraction Automatic entity normalization (coding) to 10+ medical terminologies Transfer learning for named entity recognition models Ready to use healthcare-specific word, chunk, and sentence embeddings
Visual $ Free Trial 1 License Per Year
The visual edition offers access to OCR features for image preprocessing and digitization, as well as to Visual NER based preannotation and model training. All features from the Community library Support for annotations on multi-page scanned or generated PDFs Support for image annotation (png) Text search in image documents Multi line chunk annotations Sticky token selection Custom region annotation Relations annotation Text preannotation in image or PDF documents Support for tuning Visual NER models with custom annotation
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