# 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.
Homepage: https://www.johnsnowlabs.com/
Docs & Demos: https://nlp.johnsnowlabs.com/
## Features
Powered by John Snow Labs Enterprise-Grade Ecosystem:
- 🚀 [Spark-NLP](https://www.johnsnowlabs.com/spark-nlp/) : State of the art NLP at scale!
- 🤖 [NLU](https://github.com/JohnSnowLabs/nlu) : 1 line of code to conquer NLP!
- 🕶 [Visual NLP](https://www.johnsnowlabs.com/visual-nlp/) : Empower your NLP with a set of eyes!
- 💊 [Healthcare NLP](https://www.johnsnowlabs.com/healthcare-nlp/) : Heal the world with NLP!
- âš– [Legal NLP](https://www.johnsnowlabs.com/legal-nlp/) : Bring justice with NLP!
- 💲 [Finance NLP](https://www.johnsnowlabs.com/finance-nlp/) : Understand Financial Markets with NLP!
- 🎨 [NLP-Display](https://github.com/JohnSnowLabs/spark-nlp-display) Visualize and Explain NLP!
- 📊 [NLP-Test](https://github.com/JohnSnowLabs/nlptest) : Deliver Reliable, Safe and Effective Models!
- 🔬 [NLP-Lab](https://www.johnsnowlabs.com/nlp-lab/) : No-Code Tool to Annotate & Train new Models!
## Installation
```python
! pip install johnsnowlabs
from johnsnowlabs import nlp
nlp.load('emotion').predict('Wow that easy!')
```
See [the documentation](https://nlp.johnsnowlabs.com/docs/en/install) for more details.
## Usage
These are examples of getting things done with one line of code.
See the [General Concepts Documentation](https://nlp.johnsnowlabs.com/docs/en/concepts) for building custom pipelines.
```python
# 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 |
```python
# 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 |
```python
# 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 |
```python
nlp.load('ner').viz('Bill goes to New York')
```
Returns:
For a full overview see the [1-liners Reference](https://nlp.johnsnowlabs.com/docs/en/examples) and [the Workshop](https://github.com/JohnSnowLabs/spark-nlp-workshop).
## Use Licensed Products
To use John Snow Labs' paid products like [Healthcare NLP](https://www.johnsnowlabs.com/healthcare-nlp), [Visual NLP], [Legal NLP], or [Finance NLP], get a license key and then call nlp.install() to use it:
```python
! 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.")
```
## Usage
These are examples of getting things done with one line of code.
See the [General Concepts Documentation](https://nlp.johnsnowlabs.com/docs/en/concepts) for building custom pipelines.
```python
# 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:
```python
# Temporal Relationship Extraction&Visualization
nlp.load('relation.temporal_events')\
.viz('The patient developed cancer after a mercury poisoning in 1999 ')
```
returns:
## Helpful Resources
Take a look at the official Johnsnowlabs page page: [https://nlp.johnsnowlabs.com](https://nlp.johnsnowlabs.com/) for user documentation and examples
| Ressource | Description|
|----------------------------------------------------------------------------------------------------------------------|-------------------------------------------|
| [General Concepts](https://nlu.johnsnowlabs.com/docs/en/concepts) | General concepts in the Johnsnowlabs library
| [Overview of 1-liners](https://nlu.johnsnowlabs.com/docs/en/examples) | Most common used models and their results
| [Overview of 1-liners for healthcare](https://nlu.johnsnowlabs.com/docs/en/examples_hc) | Most common used healthcare models and their results
| [Overview of all 1-liner Notebooks](https://nlu.johnsnowlabs.com/docs/en/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](https://join.slack.com/t/spark-nlp/shared_invite/zt-lutct9gm-kuUazcyFKhuGY3_0AMkxqA) | 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](https://github.com/JohnSnowLabs/spark-nlp/discussions) | More indepth discussion with the community? Post a thread in our discussion Forum
| [Github Issues](https://github.com/JohnSnowLabs/nlu/issues) | Report a bug
| [Custom Installation](https://nlu.johnsnowlabs.com/docs/en/install_advanced) | Custom installations, Air-Gap mode and other alternatives
| [The `nlp.load(<Model>)` function](https://nlu.johnsnowlabs.com/docs/en/load_api) | Load any model or pipeline in one line of code
| [The `nlp.load(<Model>).predict(data)` function](https://nlu.johnsnowlabs.com/docs/en/predict_api) | Predict on `Strings`, `List of Strings`, `Numpy Arrays`, `Pandas`, `Modin` and `Spark Dataframes`
| [The `nlp.load(<train.Model>).fit(data)` function](https://nlu.johnsnowlabs.com/docs/en/training) | 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](https://nlu.johnsnowlabs.com/docs/en/viz_examples) | 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](https://nlu.johnsnowlabs.com/docs/en/streamlit_viz_examples) | Display an interactive GUI which lets you explore and test every model and feature in Johnsowlabs 1-liner repertoire in 1 click.
## License
This library is licensed under the [Apache 2.0](https://github.com/JohnSnowLabs/johnsnowlabs/blob/main/LICENSE) license.
John Snow Labs' paid products are subject to this [End User License Agreement](https://www.johnsnowlabs.com/health-nlp-spark-ocr-libraries-eula/).
By calling nlp.install() to add them to your environment, you agree to its terms and conditions.
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"description": "# John Snow Labs: State-of-the-art NLP in Python\n\nThe John Snow Labs library provides a simple & unified Python API for delivering enterprise-grade natural language processing solutions:\n1. 15,000+ free NLP models in 250+ languages in one line of code. Production-grade, Scalable, trainable, and 100% open-source.\n2. Open-source libraries for Responsible AI (NLP Test), Explainable AI (NLP Display), and No-Code AI (NLP Lab).\n3. 1,000+ healthcare NLP models and 1,000+ legal & finance NLP models with a John Snow Labs license subscription.\n\nHomepage: https://www.johnsnowlabs.com/\n\nDocs & Demos: https://nlp.johnsnowlabs.com/\n\n\n## Features\n\nPowered by John Snow Labs Enterprise-Grade Ecosystem:\n\n- \ud83d\ude80 [Spark-NLP](https://www.johnsnowlabs.com/spark-nlp/) : State of the art NLP at scale!\n- \ud83e\udd16 [NLU](https://github.com/JohnSnowLabs/nlu) : 1 line of code to conquer NLP!\n- \ud83d\udd76 [Visual NLP](https://www.johnsnowlabs.com/visual-nlp/) : Empower your NLP with a set of eyes!\n- \ud83d\udc8a [Healthcare NLP](https://www.johnsnowlabs.com/healthcare-nlp/) : Heal the world with NLP!\n- \u2696 [Legal NLP](https://www.johnsnowlabs.com/legal-nlp/) : Bring justice with NLP!\n- \ud83d\udcb2 [Finance NLP](https://www.johnsnowlabs.com/finance-nlp/) : Understand Financial Markets with NLP!\n- \ud83c\udfa8 [NLP-Display](https://github.com/JohnSnowLabs/spark-nlp-display) Visualize and Explain NLP!\n- \ud83d\udcca [NLP-Test](https://github.com/JohnSnowLabs/nlptest) : Deliver Reliable, Safe and Effective Models!\n- \ud83d\udd2c [NLP-Lab](https://www.johnsnowlabs.com/nlp-lab/) : No-Code Tool to Annotate & Train new Models!\n\n## Installation\n\n```python\n! pip install johnsnowlabs\n\nfrom johnsnowlabs import nlp\nnlp.load('emotion').predict('Wow that easy!')\n\n```\n\nSee [the documentation](https://nlp.johnsnowlabs.com/docs/en/install) for more details.\n\n\n\n## Usage\n\nThese are examples of getting things done with one line of code.\nSee the [General Concepts Documentation](https://nlp.johnsnowlabs.com/docs/en/concepts) for building custom pipelines.\n\n```python\n# Example of Named Entity Recognition\nnlp.load('ner').predict(\"Dr. John Snow is an British physician born in 1813\")\n```\n\nReturns :\n\n| entities | entities_class | entities_confidence | \n|-----------|----------------|:--------------------|\n| John Snow | PERSON | 0.9746 | \n| British | NORP | 0.9928 | \n| 1813 | DATE | 0.5841 | \n\n```python\n# Example of Question Answering \nnlp.load('answer_question').predict(\"What is the capital of Paris\")\n```\n\nReturns :\n\n| text | answer | \n|-------------------------------|--------|\n| What is the capital of France | Paris | \n\n```python\n# Example of Sentiment classification\nnlp.load('sentiment').predict(\"Well this was easy!\")\n```\n\nReturns :\n\n| text | sentiment_class | sentiment_confidence | \n|---------------------|-----------------|:---------------------|\n| Well this was easy! | pos | 0.999901 | \n\n\n```python\nnlp.load('ner').viz('Bill goes to New York')\n```\nReturns: \n\nFor a full overview see the [1-liners Reference](https://nlp.johnsnowlabs.com/docs/en/examples) and [the Workshop](https://github.com/JohnSnowLabs/spark-nlp-workshop).\n\n\n## Use Licensed Products\n\nTo use John Snow Labs' paid products like [Healthcare NLP](https://www.johnsnowlabs.com/healthcare-nlp), [Visual NLP], [Legal NLP], or [Finance NLP], get a license key and then call nlp.install() to use it:\n\n\n\n```python\n! pip install johnsnowlabs\n# Install paid libraries via a browser login to connect to your account\nfrom johnsnowlabs import nlp\nnlp.install()\n# Start a licensed session\nnlp.start()\nnlp.load('en.med_ner.oncology_wip').predict(\"Woman is on chemotherapy, carboplatin 300 mg/m2.\")\n```\n\n## Usage \n\nThese are examples of getting things done with one line of code.\nSee the [General Concepts Documentation](https://nlp.johnsnowlabs.com/docs/en/concepts) for building custom pipelines.\n\n\n```python\n# visualize entity resolution ICD-10-CM codes \nnlp.load('en.resolve.icd10cm.augmented')\n .viz('Patient with history of prior tobacco use, nausea, nose bleeding and chronic renal insufficiency.')\n```\nreturns: \n\n\n\n\n```python\n# Temporal Relationship Extraction&Visualization\nnlp.load('relation.temporal_events')\\\n .viz('The patient developed cancer after a mercury poisoning in 1999 ')\n```\nreturns:\n\n\n## Helpful Resources\nTake a look at the official Johnsnowlabs page page: [https://nlp.johnsnowlabs.com](https://nlp.johnsnowlabs.com/) for user documentation and examples\n\n\n| Ressource | Description|\n|----------------------------------------------------------------------------------------------------------------------|-------------------------------------------|\n| [General Concepts](https://nlu.johnsnowlabs.com/docs/en/concepts) | General concepts in the Johnsnowlabs library\n| [Overview of 1-liners](https://nlu.johnsnowlabs.com/docs/en/examples) | Most common used models and their results\n| [Overview of 1-liners for healthcare](https://nlu.johnsnowlabs.com/docs/en/examples_hc) | Most common used healthcare models and their results \n| [Overview of all 1-liner Notebooks](https://nlu.johnsnowlabs.com/docs/en/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,\n| [Connect with us on Slack](https://join.slack.com/t/spark-nlp/shared_invite/zt-lutct9gm-kuUazcyFKhuGY3_0AMkxqA) | Problems, questions or suggestions? We have a very active and helpful community of over 2000+ AI enthusiasts putting Johnsnowlabs products to good use\n| [Discussion Forum](https://github.com/JohnSnowLabs/spark-nlp/discussions) | More indepth discussion with the community? Post a thread in our discussion Forum\n| [Github Issues](https://github.com/JohnSnowLabs/nlu/issues) | Report a bug\n| [Custom Installation](https://nlu.johnsnowlabs.com/docs/en/install_advanced) | Custom installations, Air-Gap mode and other alternatives \n| [The `nlp.load(<Model>)` function](https://nlu.johnsnowlabs.com/docs/en/load_api) | Load any model or pipeline in one line of code\n| [The `nlp.load(<Model>).predict(data)` function](https://nlu.johnsnowlabs.com/docs/en/predict_api) | Predict on `Strings`, `List of Strings`, `Numpy Arrays`, `Pandas`, `Modin` and `Spark Dataframes`\n| [The `nlp.load(<train.Model>).fit(data)` function](https://nlu.johnsnowlabs.com/docs/en/training) | Train a text classifier for `2-Class`, `N-Classes` `Multi-N-Classes`, `Named-Entitiy-Recognition` or `Parts of Speech Tagging`\n| [The `nlp.load(<Model>).viz(data)` function](https://nlu.johnsnowlabs.com/docs/en/viz_examples) | Visualize the results of `Word Embedding Similarity Matrix`, `Named Entity Recognizers`, `Dependency Trees & Parts of Speech`, `Entity Resolution`,`Entity Linking` or `Entity Status Assertion` \n| [The `nlp.load(<Model>).viz_streamlit(data)` function](https://nlu.johnsnowlabs.com/docs/en/streamlit_viz_examples) | Display an interactive GUI which lets you explore and test every model and feature in Johnsowlabs 1-liner repertoire in 1 click.\n\n\n## License\nThis library is licensed under the [Apache 2.0](https://github.com/JohnSnowLabs/johnsnowlabs/blob/main/LICENSE) license.\nJohn Snow Labs' paid products are subject to this [End User License Agreement](https://www.johnsnowlabs.com/health-nlp-spark-ocr-libraries-eula/). \nBy calling nlp.install() to add them to your environment, you agree to its terms and conditions.\n\n\n",
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