Skip to main content
was successfully added to your cart.

Spark NLP for Finance is Released!

Spark NLP For Finance 1.0.0: New state-of-the-art models in multiple languages!

 

Overview

Our team at John Snow Labs has great combined experience bringing leading NLP library to market and supporting our clients. 50% of the BIFS (Business, Insurance, Financial Services) use Spark NLP in Production. Downloaded more than 25 million times and experiencing 10x growth over the last year, Spark NLP is used by 41% of healthcare organizations as the world’s most widely used NLP library in the enterprise

Spark NLP in Industry

It was time to transfer our experience in the field of healthcare to the field of finance. While doing this, we carried out a meticulous study by considering the sector’s expectations.

And, we are delighted to announce Spark NLP For Finance with new state-of-the-art models available in multiple languages. This is an addition to the Spark NLP ecosystem, Including the Open Source core ( Spark NLP ), Spark NLP for Healthcare, and Spark NLP for Legal. We are so proud to share this with our community!

 

 

Spark NLP For Finance works on top of Spark NLP, which means it is also scalable to clusters and includes State-of-the-Art Transformer Architectures and models to achieve the best performance on Financial documents.

 

Spark NLP for Financial Overview

 

Major features

Spark NLP for Finance, as well as Spark NLP for Healthcare and Legal, is based on 4 main pillars:

  • Financial Named Entity Recognition: including models for extracting organizations, products, revenue, profit, losses, trading symbols, SEC 10-K information, and much more.
  • Entity-linking: to normalize NER entities and link them to public databases/data sources, such as Edgar, Crunchbase, and Nasdaq. By doing that, you can augment Company Names, for example, with externally available data about them.
  • Assertion Status: to infer temporality (present, past, future), probability (possible), or other conditions in the context of the extracted entities
  • Relation Extraction: to infer relations between the extracted entities. For example, the relations of the parties in an agreement.

 

You can carry out many more NLP Tasks, such as:

  • Financial Embeddings: The result is an n-dimensional embedding matrix, impossible to process by the human eye, containing the interpretation of the text using a financial domain.
  • Question Answering: Financial Question Answering model, finetuned on proprietary Financial questions and answers.
  • Text Classification: to classify texts into specific financial categories.
  • Document Splitting: Split your long documents at paragraph or sentence level, or use NER to detect sections and then split by them at the section level.
  • Sentiment Analysis: to identify positive, negative, or neutral sentiments in financial news.
  • Pattern Matching: Use context-aware symbolic components to model patterns to combine with Deep Learning information extraction techniques.
  • Knowledge Graphs: Create Knowledge bases combining entities and relations in a graph, which can be exploited afterward in graph databases.
  • Deidentification: The task of detecting privacy-related entities in text, such as person names, emails, and contact data.
  • Table Understanding: Use State-of-the-Art Deep Learning architectures to query tables (extracted, for example, with Spark OCR) with Natural Language. No training is needed.
  • Zero-shot Learning: Spark NLP for Finance includes Zero-shot NER and Zero-shot Relation Extraction, to create your information extraction models without any training data, just with examples (prompts).

 

Models and Demos

New Models

Models for the following tasks are available in Spark NLP For Finance:
Named Entity RecognitionText ClassificationImage ClassificationSentiment AnalysisQuestion AnsweringChunk MappingRelation ExtractionAssertion StatusEntity ResolutionDe-identification.

We also provide information such as the metricsbenchmarks and the code snippet for each model inside the model’s hub page to download the model.

New Demos

We have prepared a new section on our Demo site, where you will find demos, showcasing some of the models available in Models Hub.

This is a showcase of one example for each category:

 

Documentation

John Snow Labs Announces Finance NLP and Legal NLP, Bringing State-of-the-Art Natural Language Processing to New Domains

400 New Language Models Specific to Finance and Legal Applications are Now Available from John Snow Labs John Snow Labs, the healthcare...