John Snow Labs 4.3.1 Library Release

• Hotfix for bug that causes pip install to fail because of dependency conflicts from NLU

John Snow Labs 4.3.0 Library Release

• bump enterprise NLP and open source NLP to 4.3.0
• Generic Log Reg and Generic SVM available for finance, legal and medical modules
• Hubert, Swin Transformer, Zero-SHOT NER, CamemBert for QA for nlp module

John Snow Labs 4.2.9 Library Release

• New TextSplitter Annotator for Finance & Legal which is just a use-case focused alias for SentenceDetector
• Fix bug with NLP module not properly refreshing attached classes after running nlp.install()

John Snow Labs 4.2.8 Library Release

• DocMapper, DocMapperApproach, DocObfuscator, DocMlClassifier, Resolution2Chunk, ContextParserModel for finance,legal and healthcare
• Upgrade Enterprise NLP to 4.2.8
• Upgrade Open Source NLP to 4.2.8
• Upgrade Visual NLP to 4.3.0
• better error messages when importing modules fails
• Improving various error messages
• fix bug causing dependency on nbformat
• fix bugs with handling paths incorrectly on windows

John Snow Labs Library 4.2.5 Release

• visual to 4.2.4 bump
• enterprise nlp to 4.2.5 bump
• training_log_parser

John Snow Labs Library 4.2.4 Release

bump nlp-enterprise version
fix bad import mapping for some legal annotators
fix bug with setting license env variables on Databricks

johnsnowlabs-4.2.4.tar.gz

John Snow Labs Library 4.2.3 Release

• version bumps
• docstring updates
• new nlp annotators from enterprise 4.2.1

#12

John Snow Labs 4.2.2 - Spark NLP version Bump to 4.2.1

We are glad to announce johnsnowlabs 4.2.2 has been released!

Changes :

• Version Bump Spark NLP to 4.2.1
• Fix minor bug with type conversion during pypi standard instal

johnsnowlabs422.zip

John Snow Labs 4.2.1 Release - No more restarts required after installing licensed libs

We are pleased to announce that version 4.2.1 of the johnsnowlabs the library has been released!
It comes with one crucial improvement:
No more notebook restarts required after running jsl.install()

4.2.1johnsnowlabs.zip

John Snow Labs Library 4.2.0 Release

We are announcing with incredible excitement the release of the John Snow Labs 4.2.0 Library!
It introduces

• New Enterprise Syntax to easily access any feature of any JSL-Library.
• Highly configurable Automatic Installers with various Authorization Flows and Installation Targets like 1-Click OAUTH, 1 Line Databricks, 1 Line for new Enterprise Compatible venv and extended Offline support.
• Easily run a Python Function, Raw Python Code Snippet, Python Script or Python Module in a Databricks cluster in 1 line of code and create one if missing.
• Smart License/Jar/Wheel Caching, never type your license twice on the same machine when starting up a SparkSession or Re-Installing licensed libs!
• Various of Safety Mechanisms and Footguns removed, to reduce injuries :)

Introducing the new Enterprise Syntax for working with all of John Snow Labs libraries.
It bundles every relevant Function and Class you might ever need when working with JSL-Libraries into 1 simple import line.
from johnsnowlabs import *
This single import gets your thorugh all of the certification Notebooks, with exception of a few third party libraries.
The following modules will become avaiable :

Links to existing prodcuts

Usage&Overview for more details on import structure

• nlp.MyAnno() and nlp.my_function() for every of Spark NLP's Python Functions/Classes/Modules
• ocr.MyAnno() and ocr.my_function() for every of Spark OCR's Python Functions/Classes/Modules
• legal.MyAnno() and legal.my_function() for every of Spark For Legal Python Functions/Classes/Modules
• finance.MyAnno() and finance.my_function() for every of Spark For Finance Python Functions/Classes/Modules
• medical.MyAnno() and medical.my_function() for every of Spark For Medical Python Functions/Classes/Modules
• viz.MyVisualizer() for every of Spark NLP-Display Classes
• jsl.load() and jsl.viz() from NLU

New Powerful Installation and Spark Session Start

---

The John Snow Labs libary aims to make installing Licensed Libraries and starting a Sparksession as easy as possible.
Installation Docs & Launch a Spark Session Docs

• jsl.install()
  • Authorization Flows (proove you have a License):
    • Auto-Detect Environment Variables
    • Auto Detect license files in current working dir
    • Auto Detect cached license information that was stored in ~/.johnsnowlabs from previous uns
    • Auto-Inject Local Browser Based OAuth
    • Auto-Inject Colab Button based Oauth
    • Manual Variable Definition
    • Manual Json Path
    • Access Token
  • Installation Targets (Where to install to?):
    • Currently running Python Process
    • Into a Python environment, which is not the currently running Process
    • Into a provided Venv
    • Into a freshly created venv by the john snow labs library
    • Airgap, by creating easy copy-pastable Zip file with all Jar/Wheels/Licenses to run in airgap
    • Databricks
• jsl.start()
  • After having run jsl.install() you can just run jsl.start() .It remembers the license that was used to install and also has all jars pre-downloaded.
    Additionally, it gives very helpful Logs when launching a session, telling you loaded jars and their versions.
    You can even load a new license during jsl.start() , which supports all of the previously mentioned authorization flows.

License Management

List all of your usable jsl licenses with jsl.list_remote_licenses()
And your locally cached licenses with jsl.list_local_licenses()

---

Databricks Utils

Easily submit any task to a Databricks cluster, in various formats, see Utils for databricks Docs

Run a Raw Python Code String in a Cluster and also create on on the fly.

from johnsnowlabs import *
script = """
import nlu
print(nlu.load('sentiment').predict('That was easy!'))"""

cluster_id = jsl.install(json_license_path=my_license, databricks_host=my_host,databricks_token=my_token)
jsl.run_in_databricks(script,
                      databricks_cluster_id=cluster_id,
                      databricks_host=my_host,
                      databricks_token=my_token,
                      run_name='Python Code String Example')

Run a Python Function in a Cluster.

def my_function():
    import nlu
    medical_text = """A 28-year-old female with a history of gestational 
    diabetes presented with a one-week history of polyuria ,
     polydipsia , poor appetite , and vomiting ."""
    df = nlu.load('en.med_ner.diseases').predict(medical_text)
    for c in df.columns: print(df[c])

# my_function will run on databricks
jsl.run_in_databricks(my_function,
                      databricks_cluster_id=cluster_id,
                      databricks_host=my_host,
                      databricks_token=my_token,
                      run_name='Function test')

Run a Python Script in a Cluster.

jsl.run_in_databricks('path/to/my/script.py',
                      databricks_cluster_id=cluster_id,
                      databricks_host=my_host,
                      databricks_token=my_token,
                      run_name='Script test ')

Run a Python Module in a Cluster

import johnsnowlabs.auto_install.health_checks.nlp_test as nlp_test
jsl.run_in_databricks(nlp_test,
                      databricks_cluster_id=cluster_id,
                      databricks_host=my_host,
                      databricks_token=my_token,
                      run_name='nlp_test')

Testing Utils

You can use the John Snow Labs library to automatically test 10000+ models and 100+ Notebooks in 1 line of code within a small machine like a single Google Colab Instance and generate very handy error reports of potentially broken Models, Notebooks or Models hub Markdown Snippets.

Automatically test Notebooks/Modelshub Markdwon via URL, File-Path and many more options!

Workshop Notebook Testing Utils

See Utils for Testing Notebooks docs

from johnsnowlabs.utils.notebooks import test_ipynb

# Test a Local Markdown file with a Python Snippet

test_ipynb('path/to/local/notebook.ipynb')
# Test a Modelshub Python Markdown Snippet via URL 
test_ipynb('https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/tutorials/Certification_Trainings/Healthcare/5.Spark_OCR.ipynb',)


# Test a folder of Markdown Snippets and generate a Report file, which captures all stderr/stdout
test_ipynb('my/notebook/folder')


# Test an Array of URLS/Paths to Markdown Fies
test_ipynb([ 'https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/tutorials/Certification_Trainings/Healthcare/5.Spark_OCR.ipynb', 'path/to/local/notebook.ipynb',])

# Run ALL notebooks in the Certification Folder  
test_result = test_ipynb('WORKSHOP')

# Only run Finance notebooks
test_result = test_ipynb('WORKSHOP-FIN')

# Only run Legal notebooks
test_result = test_ipynb('WORKSHOP-LEG')

# Only run Medical notebooks
test_result = test_ipynb('WORKSHOP-MED')

# only run Open Source notebooks
test_result = test_ipynb('WORKSHOP-OS')

Modelshub Testing Utils

See Utils for Testing Models & Modelshub Markdown Snippets Docs

from johnsnowlabs.utils.modelhub_markdown import test_markdown

# Test a Local Markdown file with a Python Snippet
test_markdown('path/to/my/file.md')
# Test a Modelshub Python Markdown Snippet via URL 
test_markdown('https://nlp.johnsnowlabs.com/2022/08/31/legpipe_deid_en.html')

# Test a folder of Markdown Snippets and generate a Report file, which captures all stderr/stdout
test_markdown('my/markdown/folder')

# Test an Array of URLS/Paths to Markdown Fies
test_ipynb(['legpipe_deid_en.html','path/to/local/markdown_snippet.md',])

New Documentation Pages

• Installation
• Launch a Spark Session
• Usage and imports overview
• Settings & Caching
• Utils for databricks
• Utils for Testing Notebooks
• Utils for Testing Models & Modelshub Markdown Snippets
• Release Notes