This release contains examples and tutorials on how to visualize the 1000+ state-of-the-art NLP models provided by NLU in just 1 line of code in streamlit.
It includes simple 1-liners you can sprinkle into your Streamlit app to for features like Dependency Trees, Named Entities (NER), text classification results, semantic simmilarity,
embedding visualizations via ELMO, BERT, ALBERT, XLNET and much more . Additionally, improvements for T5, various resolvers have been added and models Farsi, Hebrew, Korean, and Turkish

This is the ultimate NLP research tool. You can visualize and compare the results of hundreds of context aware deep learning embeddings and compare them with classical vanilla embeddings like Glove
and can see with your own eyes how context is encoded by transformer models like BERT or XLNETand many more !
Besides that, you can also compare the results of the 200+ NER models John Snow Labs provides and see how peformances changes with varrying ebeddings, like Contextual, Static and Domain Specific Embeddings.

Install

For detailed instructions refer to the NLU install documentation here
You need Open JDK 8 installed and the following python packages

pip install nlu streamlit pyspark==3.0.1 sklearn plotly

Problems? Connect with us on Slack!

Impatient and want some action?

Just run this Streamlit app, you can use it to generate python code for each NLU-Streamlit building block

streamlit run https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/examples/streamlit/01_dashboard.py

Quick Starter cheat sheet - All you need to know in 1 picture for NLU + Streamlit

For NLU models to load, see the NLU Namespace or the John Snow Labs Modelshub or go straight to the source.

Examples

Just try out any of these.
You can use the first example to generate python-code snippets which you can
recycle as building blocks in your streamlit apps!

Example: `01_dashboard`

streamlit run https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/examples/streamlit/01_dashboard.py

Example: `02_NER`

streamlit run https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/examples/streamlit/02_NER.py

Example: `03_text_similarity_matrix`

streamlit run https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/examples/streamlit/03_text_similarity_matrix.py

Example: `04_dependency_tree`

streamlit run https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/examples/streamlit/04_dependency_tree.py

Example: `05_classifiers`

streamlit run https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/examples/streamlit/05_classifiers.py

Example: `06_token_features`

streamlit run https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/examples/streamlit/06_token_features.py

How to use NLU?

All you need to know about NLU is that there is the nlu.load() method which returns a NLUPipeline object
which has a .predict() that works on most common data types in the pydata stack like Pandas dataframes .
Ontop of that, there are various visualization methods a NLUPipeline provides easily integrate in Streamlit as re-usable components. viz() method

Overview of NLU + Streamlit buildingblocks

Method	Description
`nlu.load('<Model>').predict(data)`	Load any of the 1000+ models by providing the model name any predict on most Pythontic data strucutres like Pandas, strings, arrays of strings and more
`nlu.load('<Model>').viz_streamlit(data)`	Display full NLU exploration dashboard, that showcases every feature avaiable with dropdown selectors for 1000+ models
`nlu.load('<Model>').viz_streamlit_similarity([string1, string2])`	Display similarity matrix and scalar similarity for every word embedding loaded and 2 strings.
`nlu.load('<Model>').viz_streamlit_ner(data)`	Visualize predicted NER tags from Named Entity Recognizer model
`nlu.load('<Model>').viz_streamlit_dep_tree(data)`	Visualize Dependency Tree together with Part of Speech labels
`nlu.load('<Model>').viz_streamlit_classes(data)`	Display all extracted class features and confidences for every classifier loaded in pipeline
`nlu.load('<Model>').viz_streamlit_token(data)`	Display all detected token features and informations in Streamlit
`nlu.load('<Model>').viz(data, write_to_streamlit=True)`	Display the raw visualization without any UI elements. See viz docs for more info. By default all aplicable nlu model references will be shown.
`nlu.enable_streamlit_caching()`	Enable caching the `nlu.load()` call. Once enabled, the `nlu.load()` method will automatically cached. This is recommended to run first and for large peformance gans

Detailed visualizer information and API docs

`function` `pipe.viz_streamlit`

Display a highly configurable UI that showcases almost every feature available for Streamlit visualization with model selection dropdowns in your applications.
Ths includes :

Similarity Matrix & Scalars & Embedding Information for any of the 100+ Word Embedding Models
NER visualizations for any of the 200+ Named entity recognizers
Labled & Unlabled Dependency Trees visualizations with Part of Speech Tags for any of the 100+ Part of Speech Models
Token informations predicted by any of the 1000+ models
Classification results predicted by any of the 100+ models classification models
Pipeline Configuration & Model Information & Link to John Snow Labs Modelshub for all loaded pipelines
Auto generate Python code that can be copy pasted to re-create the individual Streamlit visualization blocks.
NlLU takes the first model specified as nlu.load() for the first visualization run.
Once the Streamlit app is running, additional models can easily be added via the UI.
It is recommended to run this first, since you can generate Python code snippets to recreate individual Streamlit visualization blocks

nlu.load('ner').viz_streamlit(['I love NLU and Streamlit!','I hate buggy software'])

`function parameters` `pipe.viz_streamlit`

Argument	Type	Default	Description
`text`	`Union [str, List[str], pd.DataFrame, pd.Series]`	`'NLU and Streamlit go together like peanutbutter and jelly'`	Default text for the `Classification`, `Named Entitiy Recognizer`, `Token Information` and `Dependency Tree` visualizations
`similarity_texts`	`Union[List[str],Tuple[str,str]]`	`('Donald Trump Likes to part', 'Angela Merkel likes to party')`	Default texts for the `Text similarity` visualization. Should contain `exactly 2 strings` which will be compared `token embedding wise`. For each embedding active, a `token wise similarity matrix` and a `similarity scalar`
`model_selection`	`List[str]`	`[]`	List of nlu references to display in the model selector, see the NLU Namespace or the John Snow Labs Modelshub or go straight to the source for more info
`title`	`str`	`'NLU ❤️ Streamlit - Prototype your NLP startup in 0 lines of code🚀'`	Title of the Streamlit app
`sub_title`	`str`	`'Play with over 1000+ scalable enterprise NLP models'`	Sub title of the Streamlit app
`visualizers`	`List[str]`	`( "dependency_tree", "ner", "similarity", "token_information", 'classification')`	Define which visualizations should be displayed. By default all visualizations are displayed.
`show_models_info`	`bool`	`True`	Show information for every model loaded in the bottom of the Streamlit app.
`show_model_select`	`bool`	`True`	Show a model selection dropdowns that makes any of the 1000+ models avaiable in 1 click
`show_viz_selection`	`bool`	`False`	Show a selector in the sidebar which lets you configure which visualizations are displayed.
`show_logo`	`bool`	`True`	Show logo
`display_infos`	`bool`	`False`	Display additonal information about ISO codes and the NLU namespace structure.
`set_wide_layout_CSS`	`bool`	`True`	Whether to inject custom CSS or not.
`key`	`str`	`"NLU_streamlit"`	Key for the Streamlit elements drawn
`model_select_position`	`str`	`'side'`	Whether to output the positions of predictions or not, see `pipe.predict(positions=true`) for more info
`show_code_snippets`	`bool`	`False`	Display Python code snippets above visualizations that can be used to re-create the visualization
`num_similarity_cols`	`int`	`2`	How many columns should for the layout in Streamlit when rendering the similarity matrixes.

`function` `pipe.viz_streamlit_classes`

Visualize the predicted classes and their confidences and additional metadata to streamlit.
Aplicable with any of the 100+ classifiers

nlu.load('sentiment').viz_streamlit_classes(['I love NLU and Streamlit!','I love buggy software', 'Sign up now get a chance to win 1000$ !', 'I am afraid of Snakes','Unicorns have been sighted on Mars!','Where is the next bus stop?'])

`function parameters` `pipe.viz_streamlit_classes`

Argument	Type	Default	Description
`text`	`Union[str,list,pd.DataFrame, pd.Series, pyspark.sql.DataFrame ]`	`'I love NLU and Streamlit and sunny days!'`	Text to predict classes for. Will predict on each input of the iteratable or dataframe if type is not str.
`output_level`	`Optional[str]`	`document`	Outputlevel of NLU pipeline, see `pipe.predict()` docsmore info
`include_text_col`	`bool`	`True`	Whether to include a e text column in the output table or just the prediction data
`title`	`Optional[str]`	`Text Classification`	Title of the Streamlit building block that will be visualized to screen
`metadata`	`bool`	`False`	whether to output addition metadata or not, see `pipe.predict(meta=true)` docs for more info
`positions`	`bool`	`False`	whether to output the positions of predictions or not, see `pipe.predict(positions=true`) for more info
`set_wide_layout_CSS`	`bool`	`True`	Whether to inject custom CSS or not.
`key`	`str`	`"NLU_streamlit"`	Key for the Streamlit elements drawn
`model_select_position`	`str`	`'side'`	Whether to output the positions of predictions or not, see `pipe.predict(positions=true`) for more info
`generate_code_sample`	`bool`	`False`	Display Python code snippets above visualizations that can be used to re-create the visualization
`show_model_select`	`bool`	`True`	Show a model selection dropdowns that makes any of the 1000+ models avaiable in 1 click
`show_logo`	`bool`	`True`	Show logo
`display_infos`	`bool`	`False`	Display additonal information about ISO codes and the NLU namespace structure.

`function` `pipe.viz_streamlit_ner`

Visualize the predicted classes and their confidences and additional metadata to Streamlit.
Aplicable with any of the 250+ NER models.
You can filter which NER tags to highlight via the dropdown in the main window.

Basic usage

nlu.load('ner').viz_streamlit_ner('Donald Trump from America and Angela Merkel from Germany dont share many views')

Example for coloring

# Color all entities of class GPE black
nlu.load('ner').viz_streamlit_ner('Donald Trump from America and Angela Merkel from Germany dont share many views',colors={'PERSON':'#6e992e', 'GPE':'#000000'})

`function parameters` `pipe.viz_streamlit_ner`

Argument	Type	Default	Description
`text`	`str`	`'Donald Trump from America and Anegela Merkel from Germany do not share many views'`	Text to predict classes for.
`ner_tags`	`Optional[List[str]]`	`None`	Tags to display. By default all tags will be displayed
`show_label_select`	`bool`	`True`	Whether to include the label selector
`show_table`	`bool`	`True`	Whether show to predicted pandas table or not
`title`	`Optional[str]`	`'Named Entities'`	Title of the Streamlit building block that will be visualized to screen
`sub_title`	`Optional[str]`	`'"Recognize various Named Entities (NER) in text entered and filter them. You can select from over 100 languages in the dropdown. On the left side.",'`	Sub-title of the Streamlit building block that will be visualized to screen
`colors`	`Dict[str,str]`	`{}`	Dict with `KEY=ENTITY_LABEL` and `VALUE=COLOR_AS_HEX_CODE`,which will change color of highlighted entities.See custom color labels docs for more info.
`set_wide_layout_CSS`	`bool`	`True`	Whether to inject custom CSS or not.
`key`	`str`	`"NLU_streamlit"`	Key for the Streamlit elements drawn
`generate_code_sample`	`bool`	`False`	Display Python code snippets above visualizations that can be used to re-create the visualization
`show_model_select`	`bool`	`True`	Show a model selection dropdowns that makes any of the 1000+ models avaiable in 1 click
`model_select_position`	`str`	`'side'`	Whether to output the positions of predictions or not, see `pipe.predict(positions=true`) for more info
`show_text_input`	`bool`	`True`	Show text input field to input text in
`show_logo`	`bool`	`True`	Show logo
`display_infos`	`bool`	`False`	Display additonal information about ISO codes and the NLU namespace structure.

`function` `pipe.viz_streamlit_dep_tree`

Visualize a typed dependency tree, the relations between tokens and part of speech tags predicted.
Aplicable with any of the 100+ Part of Speech(POS) models and dep tree model

nlu.load('dep.typed').viz_streamlit_dep_tree('POS tags define a grammatical label for each token and the Dependency Tree classifies Relations between the tokens')

`function parameters` `pipe.viz_streamlit_dep_tree`

Argument	Type	Default	Description
`text`	`str`	`'Billy likes to swim'`	Text to predict classes for.
`title`	`Optional[str]`	`'Dependency Parse Tree & Part-of-speech tags'`	Title of the Streamlit building block that will be visualized to screen
`set_wide_layout_CSS`	`bool`	`True`	Whether to inject custom CSS or not.
`key`	`str`	`"NLU_streamlit"`	Key for the Streamlit elements drawn
`generate_code_sample`	`bool`	`False`	Display Python code snippets above visualizations that can be used to re-create the visualization
`set_wide_layout_CSS`	`bool`	`True`	Whether to inject custom CSS or not.
`key`	`str`	`"NLU_streamlit"`	Key for the Streamlit elements drawn
`generate_code_sample`	`bool`	`False`	Display Python code snippets above visualizations that can be used to re-create the visualization
`show_model_select`	`bool`	`True`	Show a model selection dropdowns that makes any of the 1000+ models avaiable in 1 click
`model_select_position`	`str`	`'side'`	Whether to output the positions of predictions or not, see `pipe.predict(positions=true`) for more info
`show_logo`	`bool`	`True`	Show logo
`display_infos`	`bool`	`False`	Display additonal information about ISO codes and the NLU namespace structure.

`function` `pipe.viz_streamlit_token`

Visualize predicted token and text features for every model loaded.
You can use this with any of the 1000+ models and select them from the left dropdown.

nlu.load('stemm pos spell').viz_streamlit_token('I liek pentut buttr and jelly !')

`function parameters` `pipe.viz_streamlit_token`

Argument	Type	Default	Description
`text`	`str`	`'NLU and Streamlit are great!'`	Text to predict token information for.
`title`	`Optional[str]`	`'Named Entities'`	Title of the Streamlit building block that will be visualized to screen
`show_feature_select`	`bool`	`True`	Whether to include the token feature selector
`features`	`Optional[List[str]]`	`None`	Features to to display. By default all Features will be displayed
`metadata`	`bool`	`False`	Whether to output addition metadata or not, see `pipe.predict(meta=true)` docs for more info
`output_level`	`Optional[str]`	`'token'`	Outputlevel of NLU pipeline, see `pipe.predict()` docsmore info
`positions`	`bool`	`False`	Whether to output the positions of predictions or not, see `pipe.predict(positions=true`) for more info
`set_wide_layout_CSS`	`bool`	`True`	Whether to inject custom CSS or not.
`key`	`str`	`"NLU_streamlit"`	Key for the Streamlit elements drawn
`generate_code_sample`	`bool`	`False`	Display Python code snippets above visualizations that can be used to re-create the visualization
`show_model_select`	`bool`	`True`	Show a model selection dropdowns that makes any of the 1000+ models avaiable in 1 click
`model_select_position`	`str`	`'side'`	Whether to output the positions of predictions or not, see `pipe.predict(positions=true`) for more info
`show_logo`	`bool`	`True`	Show logo
`display_infos`	`bool`	`False`	Display additonal information about ISO codes and the NLU namespace structure.

`function` `pipe.viz_streamlit_similarity`

Displays a similarity matrix, where x-axis is every token in the first text and y-axis is every token in the second text.
Index i,j in the matrix describes the similarity of token-i to token-j based on the loaded embeddings and distance metrics, based on Sklearns Pariwise Metrics.. See this article for more elaboration on similarities
Displays a dropdown selectors from which various similarity metrics and over 100 embeddings can be selected.
-There will be one similarity matrix per metric and embedding pair selected. num_plots = num_metric*num_embeddings
Also displays embedding vector information.
Applicable with any of the 100+ Word Embedding models

nlu.load('bert').viz_streamlit_word_similarity(['I love love loooove NLU! <3','I also love love looove  Streamlit! <3'])

`function parameters` `pipe.viz_streamlit_similarity`

Argument	Type	Default	Description
`texts`	`str`	`'Donald Trump from America and Anegela Merkel from Germany do not share many views.'`	Text to predict token information for.
`title`	`Optional[str]`	`'Named Entities'`	Title of the Streamlit building block that will be visualized to screen
`similarity_matrix`	`bool`	`None`	Whether to display similarity matrix or not
`show_algo_select`	`bool`	`True`	Whether to show dist algo select or not
`show_table`	`bool`	`True`	Whether show to predicted pandas table or not
`threshold`	`float`	`0.5`	Threshold for displaying result red on screen
`set_wide_layout_CSS`	`bool`	`True`	Whether to inject custom CSS or not.
`key`	`str`	`"NLU_streamlit"`	Key for the Streamlit elements drawn
`generate_code_sample`	`bool`	`False`	Display Python code snippets above visualizations that can be used to re-create the visualization
`show_model_select`	`bool`	`True`	Show a model selection dropdowns that makes any of the 1000+ models avaiable in 1 click
`model_select_position`	`str`	`'side'`	Whether to output the positions of predictions or not, see `pipe.predict(positions=true`) for more info
`write_raw_pandas`	`bool`	`False`	Write the raw pandas similarity df to streamlit
`display_embed_information`	`bool`	`True`	Show additional embedding information like `dimension`, `nlu_reference`, `spark_nlp_reference`, `sotrage_reference`, `modelhub link` and more.
`dist_metrics`	`List[str]`	`['cosine']`	Which distance metrics to apply. If multiple are selected, there will be multiple plots for each embedding and metric. `num_plots = num_metric*num_embeddings`. Can use multiple at the same time, any of of `cityblock`,`cosine`,`euclidean`,`l2`,`l1`,`manhattan`,`nan_euclidean`. Provided via Sklearn `metrics.pairwise` package
`num_cols`	`int`	`2`	How many columns should for the layout in streamlit when rendering the similarity matrixes.
`display_scalar_similarities`	`bool`	`False`	Display scalar simmilarities in an additional field.
`display_similarity_summary`	`bool`	`False`	Display summary of all similarities for all embeddings and metrics.
`show_logo`	`bool`	`True`	Show logo
`display_infos`	`bool`	`False`	Display additonal information about ISO codes and the NLU namespace structure.

In addition have added some new features to our T5 Transformer annotator to help with longer and more accurate text generation, trained some new multi-lingual models and pipelines in Farsi, Hebrew, Korean, and Turkish.

T5 Model Improvements

Add 6 new features to T5Transformer for longer and better text generation
- doSample: Whether or not to use sampling; use greedy decoding otherwise
- temperature: The value used to module the next token probabilities
- topK: The number of highest probability vocabulary tokens to keep for top-k-filtering
- topP: If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation
- repetitionPenalty: The parameter for repetition penalty. 1.0 means no penalty. See CTRL: A Conditional Transformer Language Model for Controllable Generation paper for more details
- noRepeatNgramSize: If set to int > 0, all ngrams of that size can only occur once

New Open Source Model in NLU 3.0.2

New multilingual models and pipelines for Farsi, Hebrew, Korean, and Turkish

Model	NLU Reference	Spark NLP Reference	Lang
ClassifierDLModel	`tr.classify.news`	classifierdl_bert_news	`tr`
UniversalSentenceEncoder	`xx.use.multi`	tfhub_use_multi	`xx`
UniversalSentenceEncoder	`xx.use.multi_lg`	tfhub_use_multi_lg	`xx`

Pipeline	NLU Reference	Spark NLP Reference	Lang
PretrainedPipeline	`fa.ner.dl`	recognize_entities_dl	`fa`
PretrainedPipeline	`he.explain_document`	explain_document_lg	`he`
PretrainedPipeline	`ko.explain_document`	explain_document_lg	`ko`

New Healthcare Models in NLU 3.0.2

Five new resolver models:

en.resolve.umls: This model returns CUI (concept unique identifier) codes for Clinical Findings, Medical Devices, Anatomical Structures and Injuries & Poisoning terms.
en.resolve.umls.findings: This model returns CUI (concept unique identifier) codes for 200K concepts from clinical findings.
en.resolve.loinc: Map clinical NER entities to LOINC codes using sbiobert.
en.resolve.loinc.bluebert: Map clinical NER entities to LOINC codes using sbluebert.
en.resolve.HPO: This model returns Human Phenotype Ontology (HPO) codes for phenotypic abnormalities encountered in human diseases. It also returns associated codes from the following vocabularies for each HPO code:

Related NLU Notebook

Model	NLU Reference	Spark NLP Reference
Resolver	`en.resolve.umls`	`sbiobertresolve_umls_major_concepts`
Resolver	`en.resolve.umls.findings`	`sbiobertresolve_umls_findings`
Resolver	`en.resolve.loinc`	`sbiobertresolve_loinc`
Resolver	`en.resolve.loinc.biobert`	`sbiobertresolve_loinc`
Resolver	`en.resolve.loinc.bluebert`	`sbluebertresolve_loinc`
Resolver	`en.resolve.HPO`	`sbiobertresolve_HPO`

en.resolve.HPO

nlu.load('med_ner.jsl.wip.clinical en.resolve.HPO').viz("""These disorders include cancer, bipolar disorder, schizophrenia, autism, Cri-du-chat syndrome,
 myopia, cortical cataract-linked Alzheimer's disease, and infectious diseases""")

en.resolve.loinc.bluebert

nlu.load('med_ner.jsl.wip.clinical en.resolve.loinc.bluebert').viz("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and
subsequent type two diabetes mellitus (TSS2DM), one prior episode of HTG-induced pancreatitis three years prior to presentation, associated with an acute 
hepatitis, and obesity with a body mass index (BMI) of 33.5 kg/m2, presented with a one-week history of polyuria, polydipsia, poor appetite, and vomiting.""")

en.resolve.umls.findings

nlu.load('med_ner.jsl.wip.clinical en.resolve.umls.findings').viz("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and
subsequent type two diabetes mellitus (TSS2DM), one prior episode of HTG-induced pancreatitis three years prior to presentation, associated with an acute 
hepatitis, and obesity with a body mass index (BMI) of 33.5 kg/m2, presented with a one-week history of polyuria, polydipsia, poor appetite, and vomiting."""
)

en.resolve.umls

nlu.load('med_ner.jsl.wip.clinical en.resolve.umls').viz("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and
subsequent type two diabetes mellitus (TSS2DM), one prior episode of HTG-induced pancreatitis three years prior to presentation, associated with an acute 
hepatitis, and obesity with a body mass index (BMI) of 33.5 kg/m2, presented with a one-week history of polyuria, polydipsia, poor appetite, and vomiting.""")

en.resolve.loinc

nlu.load('med_ner.jsl.wip.clinical en.resolve.loinc').predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and
subsequent type two diabetes mellitus (TSS2DM), one prior episode of HTG-induced pancreatitis three years prior to presentation, associated with an acute 
hepatitis, and obesity with a body mass index (BMI) of 33.5 kg/m2, presented with a one-week history of polyuria, polydipsia, poor appetite, and vomiting.""")

en.resolve.loinc.biobert

nlu.load('med_ner.jsl.wip.clinical en.resolve.loinc.biobert').predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and
subsequent type two diabetes mellitus (TSS2DM), one prior episode of HTG-induced pancreatitis three years prior to presentation, associated with an acute 
hepatitis, and obesity with a body mass index (BMI) of 33.5 kg/m2, presented with a one-week history of polyuria, polydipsia, poor appetite, and vomiting.""")

140+ tutorials
New Streamlit visualizations docs
The complete list of all 1100+ models & pipelines in 192+ languages is available on Models Hub.
Spark NLP publications
NLU in Action
NLU documentation
Discussions Engage with other community members, share ideas, and show off how you use Spark NLP and NLU!

1 line Install NLU on Google Colab

!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash

1 line Install NLU on Kaggle

!wget https://setup.johnsnowlabs.com/nlu/kaggle.sh -O - | bash

Install via PIP

! pip install nlu pyspark==3.0.1

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Streamlit visualizations, improved T5, models for Farsi, Hebrew, Korean, Turkish and UMLS, LOINC, HPO, Resolvers in NLU 3.0.2

Install

Impatient and want some action?

Quick Starter cheat sheet - All you need to know in 1 picture for NLU + Streamlit

Examples

Example: `01_dashboard`

Example: `02_NER`

Example: `03_text_similarity_matrix`

Example: `04_dependency_tree`

Example: `05_classifiers`

Example: `06_token_features`

How to use NLU?

Overview of NLU + Streamlit buildingblocks

Detailed visualizer information and API docs

`function` `pipe.viz_streamlit`

`function parameters` `pipe.viz_streamlit`

`function` `pipe.viz_streamlit_classes`

`function parameters` `pipe.viz_streamlit_classes`

`function` `pipe.viz_streamlit_ner`

`function parameters` `pipe.viz_streamlit_ner`

`function` `pipe.viz_streamlit_dep_tree`

`function parameters` `pipe.viz_streamlit_dep_tree`

`function` `pipe.viz_streamlit_token`

`function parameters` `pipe.viz_streamlit_token`

`function` `pipe.viz_streamlit_similarity`

`function parameters` `pipe.viz_streamlit_similarity`

T5 Model Improvements

New Open Source Model in NLU 3.0.2

New Healthcare Models in NLU 3.0.2

1 line Install NLU on Google Colab

1 line Install NLU on Kaggle

Install via PIP

Streamlit visualizations, improved T5, models for Farsi, Hebrew, Korean, Turkish and UMLS, LOINC, HPO, Resolvers in NLU 3.0.2

Install

Impatient and want some action?

Quick Starter cheat sheet - All you need to know in 1 picture for NLU + Streamlit

Examples

Example: 01_dashboard

Example: 02_NER

Example: 03_text_similarity_matrix

Example: 04_dependency_tree

Example: 05_classifiers

Example: 06_token_features

How to use NLU?

Overview of NLU + Streamlit buildingblocks

Detailed visualizer information and API docs

function pipe.viz_streamlit

function parameters pipe.viz_streamlit

function pipe.viz_streamlit_classes

function parameters pipe.viz_streamlit_classes

function pipe.viz_streamlit_ner

function parameters pipe.viz_streamlit_ner

function pipe.viz_streamlit_dep_tree

function parameters pipe.viz_streamlit_dep_tree

function pipe.viz_streamlit_token

function parameters pipe.viz_streamlit_token

function pipe.viz_streamlit_similarity

function parameters pipe.viz_streamlit_similarity

T5 Model Improvements

New Open Source Model in NLU 3.0.2

New Healthcare Models in NLU 3.0.2

1 line Install NLU on Google Colab

1 line Install NLU on Kaggle

Install via PIP

Example: `01_dashboard`

Example: `02_NER`

Example: `03_text_similarity_matrix`

Example: `04_dependency_tree`

Example: `05_classifiers`

Example: `06_token_features`

`function` `pipe.viz_streamlit`

`function parameters` `pipe.viz_streamlit`

`function` `pipe.viz_streamlit_classes`

`function parameters` `pipe.viz_streamlit_classes`

`function` `pipe.viz_streamlit_ner`

`function parameters` `pipe.viz_streamlit_ner`

`function` `pipe.viz_streamlit_dep_tree`

`function parameters` `pipe.viz_streamlit_dep_tree`

`function` `pipe.viz_streamlit_token`

`function parameters` `pipe.viz_streamlit_token`

`function` `pipe.viz_streamlit_similarity`

`function parameters` `pipe.viz_streamlit_similarity`