Demos
Example of Visual NER of Receipts with Finance and Visual NLP
John Snow Labs Finance NLP 1.12 comes with a lot of new capabilities added to the 155+ models and 40+ Language Models already available in previous versions of the library. Let’s take a look at each of them!
Native Financial Text Generation
With our new TextGeneration annotator, you can give the start of a sentence from a financial document and get the model generate the rest.
We have trained 2 models:
1️⃣A generic one, with different financial documents;
2️⃣A specific one, trained on SEC filings;
Example of the SEC-based model:
Input: Market for registrant’s common equity, related stockholder matters and issuer purchases of equity securities these forward-looking statements are subject to a number of risks, uncertainties and assumptions, including those described in “risk factors” and elsewhere in this annual report on form 10-k.
Output:
Moreover, we operate in a very competitive and rapidly changing environment new risks emerge from time to time it is not possible for our management to predict all risks, nor can we assess the impact of all factors on our business or the extent to which any factor, or combination of factors, may cause actual results to differ materially from those contained in any forward-looking statements we may make in light of these risks, uncertainties and assumptions, the forward-looking events and circumstances discussed in this annual report on form 10-k may not occur and actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements based on the closing price of the registrant’s common stock on the last business day of the registrant’s most recently completed…
Visual NER and QA demos on financial documents
We include 2 Visual NER demos (Visual NLP required) to showcase the ability to train and infer entities directly on images
- First demo, NER. It showcases a model trained on receipts, with labels as Name (of the item), Total, Subtotal, Item Subtotal, Cash, Service Price, etc.
Visual NLP trained on CORD (receipts)
- Second,Visual QA on ID documents (passport, driving license, ID cards, etc)
What is the Issue Date? 09 OCT /OCT 96
What is the Nationality? BRITISH
What are the Given names and surnames? EARLAND ANTHONY SCOTT
New notebook: Financial Summarization
In our workshop repo, with more than 40 notebooks, you will find our new notebook explaining how to use our new Financial Summarization annotator on different financial information as broker reports, capital calls, filings, responsibility reports, etc.
Have a quick glance at everything we have in Finance NLP…
…with a demo app we have published for you under or demos section, called Finance NLP Overview.
Deidentification Helper notebook
As part of the johnsnowlabs library, a new helper module has been included to make your deidentification even more easy to use. You can find it in the workshop repo as Deidentification Utility Module.
It includes examples of how to use masking and obfuscation in unstructured texts, structured tables, how to configure the mask length and symbols, a custom vocabulary for obfuscation, data shifts, and much more!
Suspicious Activity Reports
Suspicious Activity Report (SAR) is a document that financial institutions, and those associated with their business, must file with the Financial Crimes Enforcement Network (FinCEN) whenever there is a suspected case of money laundering or fraud. These reports are tools to help monitor any activity within finance-related industries that is deemed out of the ordinary, a precursor of illegal activity, or might threaten public safety.
We are happy to announce a new NER and a Pipeline for SAR.
The NER model helps you identify items and activities which are defined as suspicious. It also returns the suspicious keywords.
The Pipeline includes the NER and some additional models to detect generic entities (as dates, people, organizations, etc) on top of SAR-specific ones.
Fancy trying?
We’ve got 30-days free licenses for you with technical support from our financial team of technical and SME. This trial includes complete access to more than 150 models, including Classification, NER, Relation Extraction, Similarity Search, Summarization, Sentiment Analysis, Question Answering, etc. and 50+ financial language models.
Just go to https://www.johnsnowlabs.com/install/ and follow the instructions!
Don’t foget to check our notebooks and demos.
How to run
Finance NLP is very easy to run on both clusters and driver-only environments using johnsnowlabs library:
!pip install johnsnowlabs
nlp.install(force_browser=True) nlp.start()
