The John Snow Labs Library gives you access to all of John Snow Labs Enterprise And Open Source products in an easy and simple manner. Access 10000+ state-of-the-art NLP and OCR models for Finance, Legal and Medical domains. Easily scalable to Spark Cluster
Readme
The John Snow Labs library provides a simple & unified Python API for delivering enterprise-grade natural language processing solutions:
Homepage: https://www.johnsnowlabs.com/
Docs & Demos: https://nlp.johnsnowlabs.com/
Powered by John Snow Labs Enterprise-Grade Ecosystem:
! pip install johnsnowlabs
from johnsnowlabs import nlp
nlp.load('emotion').predict('Wow that was easy!')
See the documentation for more details.
These are examples of getting things done with one line of code. See the General Concepts Documentation for building custom pipelines.
# Example of Named Entity Recognition
nlp.load('ner').predict("Dr. John Snow is an British physician born in 1813")
Returns :
| entities | entities_class | entities_confidence |
|---|---|---|
| John Snow | PERSON | 0.9746 |
| British | NORP | 0.9928 |
| 1813 | DATE | 0.5841 |
# Example of Question Answering
nlp.load('answer_question').predict("What is the capital of Paris")
Returns :
| text | answer |
|---|---|
| What is the capital of France | Paris |
# Example of Sentiment classification
nlp.load('sentiment').predict("Well this was easy!")
Returns :
| text | sentiment_class | sentiment_confidence |
|---|---|---|
| Well this was easy! | pos | 0.999901 |
nlp.load('ner').viz('Bill goes to New York')
Returns:
For a full overview see the 1-liners Reference and the Workshop.
To use John Snow Labs' paid products like Healthcare NLP, [Visual NLP], [Legal NLP], or [Finance NLP], get a license key and then call nlp.install() to use it:
! pip install johnsnowlabs
# Install paid libraries via a browser login to connect to your account
from johnsnowlabs import nlp
nlp.install()
# Start a licensed session
nlp.start()
nlp.load('en.med_ner.oncology_wip').predict("Woman is on chemotherapy, carboplatin 300 mg/m2.")
These are examples of getting things done with one line of code. See the General Concepts Documentation for building custom pipelines.
# visualize entity resolution ICD-10-CM codes
nlp.load('en.resolve.icd10cm.augmented')
.viz('Patient with history of prior tobacco use, nausea, nose bleeding and chronic renal insufficiency.')
returns:
# Temporal Relationship Extraction&Visualization
nlp.load('relation.temporal_events')\
.viz('The patient developed cancer after a mercury poisoning in 1999 ')
returns:
Take a look at the official Johnsnowlabs page page: https://nlp.johnsnowlabs.com for user documentation and examples
| Resource | Description |
|---|---|
| General Concepts | General concepts in the Johnsnowlabs library |
| Overview of 1-liners | Most common used models and their results |
| Overview of 1-liners for healthcare | Most common used healthcare models and their results |
| Overview of all 1-liner Notebooks | 100+ tutorials on how to use the 1 liners on text datasets for various problems and from various sources like Twitter, Chinese News, Crypto News Headlines, Airline Traffic communication, Product review classifier training, |
| Connect with us on Slack | Problems, questions or suggestions? We have a very active and helpful community of over 2000+ AI enthusiasts putting Johnsnowlabs products to good use |
| Discussion Forum | More indepth discussion with the community? Post a thread in our discussion Forum |
| Github Issues | Report a bug |
| Custom Installation | Custom installations, Air-Gap mode and other alternatives |
The nlp.load(<Model>) function | Load any model or pipeline in one line of code |
The nlp.load(<Model>).predict(data) function | Predict on Strings, List of Strings, Numpy Arrays, Pandas, Modin and Spark Dataframes |
The nlp.load(<train.Model>).fit(data) function | Train a text classifier for 2-Class, N-Classes Multi-N-Classes, Named-Entitiy-Recognition or Parts of Speech Tagging |
The nlp.load(<Model>).viz(data) function | Visualize the results of Word Embedding Similarity Matrix, Named Entity Recognizers, Dependency Trees & Parts of Speech, Entity Resolution,Entity Linking or Entity Status Assertion |
The nlp.load(<Model>).viz_streamlit(data) function | Display an interactive GUI which lets you explore and test every model and feature in Johnsowlabs 1-liner repertoire in 1 click. |
This library is licensed under the Apache 2.0 license.
John Snow Labs' paid products are subject to this End User License Agreement.
By calling nlp.install() to add them to your environment, you agree to its terms and conditions.
FAQs
The John Snow Labs Library gives you access to all of John Snow Labs Enterprise And Open Source products in an easy and simple manner. Access 10000+ state-of-the-art NLP and OCR models for Finance, Legal and Medical domains. Easily scalable to Spark Cluster
We found that johnsnowlabs demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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