John Snow Labs Finance and Legal NLP Libraries are designed to help organizations extract insights from unstructured documents and enable faster, more accurate data analysis.

About the offer

This product includes a package of Natural Language Processing Python libraries specific for the Finance and Legal domains. It allows quick and easy text annotation with pre-trained DL models, rules, and prompts but also NLP model testing, training, and tuning.

There is no limit on the number of documents, models, or pipelines that can be used with this subscription: the software is licensed on a per-server basis.

For enterprise licenses contact us here

What is included

• Spark NLP, the most widely used NLP library in the Enterprise. It provides production-grade, scalable, and trainable versions of the latest research in natural language processing and access to 700+ Embeddings, and 11,000+ pretrained models and pipelines covering tasks such as Entity Recognition, Information Extraction, Spelling and Grammar, Text Classification, Translation, Summarization, Question Answering or Emotion Detection.
• Finance NLP software and models, enabling financial text classification, financial sentiment analysis, financial Named Entity Recognition (e.g. organizations, products, revenue, profit, losses, trading symbols, etc.), Entity-linking for normalizing NER entities and linking them to databases such as Edgar, Crunchbase, and Nasdaq, Assertion Status for inferring temporality and Relation Extraction financial De-identification and more.
• Legal NLP software and models, covering Named Entity Recognition, Information Extraction on clauses, Legal Clause Classification, Legal Relationship Extraction, Entity Linking, Legal De-identification Assertion Status, and Relation Extraction. It includes access to over 300+ new state-of-the-art models available in multiple languages.
• Visual NLP (OCR) software and models, enabling form understanding, table detection and extraction, noisy image enhancement, visual document classification, visual entity recognition, DICOM to text, signature detection, and image de-identification.
• Full access to all Finance, Legal and Visual models and pipelines published on the NLP Models Hub (currently 1,000+ and counting).
• Ready-to-use Jupyter notebooks that will help you get started with text and image analysis on all major NLP tasks such as text classification, sentiment analysis, named entity recognition, relation extraction, assertion status, entity linking, de-identification, translation, summarization, question answering, spelling and grammar.
• John Snow Labs python library for text understanding that can be used to test models and pipelines with one line of code.
• Spark NLP Display library for out-of-the-box annotation display on top of textual content.

Who is this offer for

• Teams of python developers that need to extract entities and relations from text, image, and pdf documents;
• Data scientists who deal with NLP problems;
• Machine learning engineers who need to test/train/tune NLP models;
• Scientific researcher groups who need to extract meaning from unstructured, natural language documents;
• And anyone else interested in text and image analysis, image digitization, data extraction, document labeling and/or NLP model training.

Target verticals

The Spark NLP and Visual NLP libraries included in this offer are general and can be applied to any domain to documents written in over 250 languages. The Finance and Legal libraries contained pretrained resources specific for the Finance and Legal domains. The NLP Models Hub contains over 12k pre-trained models and pipelines for general-purpose documents. It also contains 1000+ specialized pre-trained models for the Finance and Legal verticals.

Technical Specifications

• Recommended memory: 32GB RAM
• Recommended vCPU:8 vCPUs
• Operating System:Ubuntu 20.04

Included integrations

• Jupyter Lab is preinstalled and accessible at http://IP_ADDRESS/jupyter. Password: INSTANCE_ID

3 Easy Steps to get started

1. Subscribe to the product on the AWS Marketplace.
2. Deploy it on a new machine.
3. Access the welcome page for a guided experience on http://INSTANCE_IP.