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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

Project description

John Snow Labs: State-of-the-art NLP in Python

The John Snow Labs library provides a simple & unified Python API for delivering enterprise-grade natural language processing solutions:

  1. 15,000+ free NLP models in 250+ languages in one line of code. Production-grade, Scalable, trainable, and 100% open-source.
  2. Open-source libraries for Responsible AI (NLP Test), Explainable AI (NLP Display), and No-Code AI (NLP Lab).
  3. 1,000+ healthcare NLP models and 1,000+ legal & finance NLP models with a John Snow Labs license subscription.


Docs & Demos:


Powered by John Snow Labs Enterprise-Grade Ecosystem:

  • 🚀 Spark-NLP : State of the art NLP at scale!
  • 🤖 NLU : 1 line of code to conquer NLP!
  • 🕶 Visual NLP : Empower your NLP with a set of eyes!
  • 💊 Healthcare NLP : Heal the world with NLP!
  • Legal NLP : Bring justice with NLP!
  • 💲 Finance NLP : Understand Financial Markets with NLP!
  • 🎨 NLP-Display Visualize and Explain NLP!
  • 📊 NLP-Test : Deliver Reliable, Safe and Effective Models!
  • 🔬 NLP-Lab : No-Code Tool to Annotate & Train new Models!


! 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')

ner_viz_opensource For a full overview see the 1-liners Reference and the Workshop.

Use Licensed Products

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
# Start a licensed session
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 
    .viz('Patient with history of prior tobacco use, nausea, nose bleeding and chronic renal insufficiency.')


# Temporal Relationship Extraction&Visualization
    .viz('The patient developed cancer after a mercury poisoning in 1999 ')

returns: relationv_viz

Helpful Resources

Take a look at the official Johnsnowlabs page page: 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.

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