Demos
Finance NLP 1.10 comes with a lot of new capabilities added to the 135+ models and 25+ Language Models already available in previous versions of the library. Let’s take a look at each part of NLP for financial services!
Spanish Financial Sentiment Analysis
We have participated in the CodeLab competition for Financial NER and Sentiment Analysis on Spanish texts, training models for the following two tasks:
Target detection. The identification of the economic target of the headline is hindered by the reduced length of the text and the linguistic features of newspaper headlines.
Multi-dimension sentiment classification. As opposed to traditional multi-target tasks in which multiple targets are identified within the scope of each individual processed text, here each news headline refers to a single target entity, but the stances of other economic agents (companies and consumers) are also considered.
As a result, 1 NER model and 3 classifiers (NER target sentiment, consumer sentiment and company sentiment) have been published in our Models Hub repo.
Financial Chinese Sentence Embeddings
Bert and DistilBert Sentence Embeddings provide you with numerical representations (embeddings) of sentences in context. This allows you to calculate the similarity between different financial texts, train classifiers, and cluster your texts. Example of cosine similarity of 8千日利息400元?
Augmented Broker Reports NER
Our Broker Reports model has been augmented with more data.
New demos: Responsibility Reports and QA
In our demo section, you will find three new demos:
Understanding Entities in Context: Detecting if an amount is mentioned to be increased or decreased.
Amounts increasing
Amounts decreasing
Financial Table Question Answering.
German NER and Relation Extraction
Detect financial values and financial entities in German Financial Reports.
Also, use Relation Extraction to link those values to their related entities.
Solution Accelerator: Responsibility Reports
A series of notebooks (also known as solution accelerator) have been published here to carry out a series of NLP tasks regarding Responsibility Reports (RR).
- Visual NLP to extract text from PDF.
- Text Classification models about ESG (Environment, Social, Governance) with 20+ classes.
- Named Entity Recognition on RR
- Table detection and extraction
- Table Question Answering
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 135 models, including Classification, NER, Relation Extraction, Similarity Search, Summarization, Sentiment Analysis, Question Answering, etc. and 25+ financial language models.
Just go to https://www.johnsnowlabs.com/install/ and follow the instructions!
Don’t foger to check our notebooks and NLP demo.
How to run
John Snow Lab`s natural language processing for Finance 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()
