Streamlit visualization Examples

 

This page 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 and various resolvers have been added.

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! z

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 spellbook or the John Snow Labs Modelshub or go straight to the source. NLU Streamlit Cheatsheet

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

Example: 07_token_embedding_dimension_reduction

streamlit run https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/examples/streamlit/07_token_embedding_manifolds.py

Example: 08_token_embedding_dimension_reduction

streamlit run https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/examples/streamlit/08_sentence_embedding_manifolds.py

Example: 09_entity_embedding_dimension_reduction

streamlit run https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/examples/streamlit/09_entity_embedding_manifolds.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'])

NLU Streamlit UI Overview

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 spellbook 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 spellbook 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?'])

text_class1

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 spellbook 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')

NER visualization

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'})

NER coloring

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 spellbook 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')

Dependency Tree

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 spellbook 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 !')

text_class1

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 spellbook 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'])

text_class1

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 spellbook structure.

Embedding visualization via Manifold and Matrix Decomposition algorithms

function pipe.viz_streamlit_word_embed_manifold

Visualize Word Embeddings in 1-D, 2-D, or 3-D by Reducing Dimensionality via 11 Supported methods from Manifold Algorithms and Matrix Decomposition Algorithms. Additionally, you can color the lower dimensional points with a label that has been previously assigned to the text by specifying a list of nlu references in the additional_classifiers_for_coloring parameter.

nlu.load('bert',verbose=True).viz_streamlit_word_embed_manifold(default_texts=['I love NLU <3',  'I love streamlit <3'],default_algos_to_apply=['TSNE'],MAX_DISPLAY_NUM=5)

function parameters pipe.viz_streamlit_word_embed_manifold

Argument Type Default Description  
default_texts List[str] (“Donald Trump likes to party!”, “Angela Merkel likes to party!”, ‘Peter HATES TO PARTTY!!!! :(‘) List of strings to apply classifiers, embeddings, and manifolds to.  
text Optional[str] 'Billy likes to swim' Text to predict classes for.  
sub_title Optional[str] “Apply any of the 11 Manifold or Matrix Decomposition algorithms to reduce the dimensionality of Word Embeddings to 1-D, 2-D and 3-D Sub title of the Streamlit app  
default_algos_to_apply List[str] ["TSNE", "PCA"] A list Manifold and Matrix Decomposition Algorithms to apply. Can be either 'TSNE','ISOMAP','LLE','Spectral Embedding', 'MDS','PCA','SVD aka LSA','DictionaryLearning','FactorAnalysis','FastICA' or 'KernelPCA',  
target_dimensions List[int] (1,2,3) Defines the target dimension embeddings will be reduced to  
show_algo_select bool True Show selector for Manifold and Matrix Decomposition Algorithms  
show_embed_select bool True Show selector for Embedding Selection  
show_color_select bool True Show selector for coloring plots  
MAX_DISPLAY_NUM int 100 Cap maximum number of Tokens displayed  
display_embed_information bool True Show additional embedding information like dimension, nlu_reference, spark_nlp_reference, sotrage_reference, modelhub link and more.  
set_wide_layout_CSS bool True Whether to inject custom CSS or not.  
num_cols int 2 How many columns should for the layout in streamlit when rendering the similarity matrixes.  
key str "NLU_streamlit" Key for the Streamlit elements drawn  
additional_classifiers_for_coloring List[str] ['pos', 'sentiment.imdb'] List of additional NLU references to load for generting hue colors  
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.  
n_jobs Optional[int] 3 False How many cores to use for paralellzing when using Sklearn Dimension Reduction algorithms.

Larger Example showcasing more dimension reduction techniques on a larger corpus :

function pipe.viz_streamlit_sentence_embed_manifold

Visualize Sentence Embeddings in 1-D, 2-D, or 3-D by Reducing Dimensionality via 12 Supported methods from Manifold Algorithms and Matrix Decomposition Algorithms. Additionally, you can color the lower dimensional points with a label that has been previously assigned to the text by specifying a list of nlu references in the additional_classifiers_for_coloring parameter. You can also select additional classifiers via the GUI.

  • Reduces Dimensionality of high dimensional Sentence Embeddings to 1-D, 2-D, or 3-D and plot the resulting data in an interactive Plotly plot
  • Applicable with any of the 100+ Sentence Embedding models
  • Color points by classifying with any of the 100+ Document Classifiers
  • Gemerates NUM-DIMENSIONS * NUM-EMBEDDINGS * NUM-DIMENSION-REDUCTION-ALGOS plots
nlu.load('embed_sentence.bert').viz_streamlit_sentence_embed_manifold(['text1','text2tdo'])

function parameters pipe.viz_streamlit_sentence_embed_manifold

Argument Type Default Description  
default_texts List[str] (“Donald Trump likes to party!”, “Angela Merkel likes to party!”, ‘Peter HATES TO PARTTY!!!! :(‘) List of strings to apply classifiers, embeddings, and manifolds to.  
text Optional[str] 'Billy likes to swim' Text to predict classes for.  
sub_title Optional[str] “Apply any of the 11 Manifold or Matrix Decomposition algorithms to reduce the dimensionality of Sentence Embeddings to 1-D, 2-D and 3-D Sub title of the Streamlit app  
default_algos_to_apply List[str] ["TSNE", "PCA"] A list Manifold and Matrix Decomposition Algorithms to apply. Can be either 'TSNE','ISOMAP','LLE','Spectral Embedding', 'MDS','PCA','SVD aka LSA','DictionaryLearning','FactorAnalysis','FastICA' or 'KernelPCA',  
target_dimensions List[int] (1,2,3) Defines the target dimension embeddings will be reduced to  
show_algo_select bool True Show selector for Manifold and Matrix Decomposition Algorithms  
show_embed_select bool True Show selector for Embedding Selection  
show_color_select bool True Show selector for coloring plots  
display_embed_information bool True Show additional embedding information like dimension, nlu_reference, spark_nlp_reference, sotrage_reference, modelhub link and more.  
set_wide_layout_CSS bool True Whether to inject custom CSS or not.  
num_cols int 2 How many columns should for the layout in streamlit when rendering the similarity matrixes.  
key str "NLU_streamlit" Key for the Streamlit elements drawn  
additional_classifiers_for_coloring List[str] ['sentiment.imdb'] List of additional NLU references to load for generting hue colors  
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.  
n_jobs Optional[int] 3 False How many cores to use for paralellzing when using Sklearn Dimension Reduction algorithms.

Streamlit Entity Manifold visualization

function pipe.viz_streamlit_entity_embed_manifold

Visualize recognized entities by NER models via their Entity Embeddings in 1-D, 2-D, or 3-D by Reducing Dimensionality via 10+ Supported methods from Manifold Algorithms and Matrix Decomposition Algorithms. You can pick additional NER models and compare them via the GUI dropdown on the left.

  • Reduces Dimensionality of high dimensional Entity Embeddings to 1-D, 2-D, or 3-D and plot the resulting data in an interactive Plotly plot
  • Applicable with any of the 330+ Named Entity Recognizer models
  • Gemerates NUM-DIMENSIONS * NUM-NER-MODELS * NUM-DIMENSION-REDUCTION-ALGOS plots
nlu.load('ner').viz_streamlit_sentence_embed_manifold(['Hello From John Snow Labs', 'Peter loves to visit New York'])

function parameters pipe.viz_streamlit_sentence_embed_manifold

| Argument | Type | Default |Description | |—————————-|————|———————————————————–|———————————————————| |default_texts| List[str] |”Donald Trump likes to visit New York”, “Angela Merkel likes to visit Berlin!”, ‘Peter hates visiting Paris’)| List of strings to apply classifiers, embeddings, and manifolds to. |
| title | str | 'NLU ❤️ Streamlit - Prototype your NLP startup in 0 lines of code🚀' | Title of the Streamlit app |sub_title| Optional[str] | “Apply any of the 10+ Manifold or Matrix Decomposition algorithms to reduce the dimensionality of Entity Embeddings to 1-D, 2-D and 3-D “ | Sub title of the Streamlit app |
|default_algos_to_apply| List[str] | ["TSNE", "PCA"] | A list Manifold and Matrix Decomposition Algorithms to apply. Can be either 'TSNE','ISOMAP','LLE','Spectral Embedding', 'MDS','PCA','SVD aka LSA','DictionaryLearning','FactorAnalysis','FastICA' or 'KernelPCA', |
|target_dimensions| List[int] | (1,2,3) | Defines the target dimension embeddings will be reduced to | |show_algo_select| bool | True | Show selector for Manifold and Matrix Decomposition Algorithms |
| set_wide_layout_CSS | bool | True | Whether to inject custom CSS or not.|
|num_cols | int | 2 | How many columns should for the layout in streamlit when rendering the similarity matrixes.|
| key | str | "NLU_streamlit" | Key for the Streamlit elements drawn | | show_logo | bool | True | Show logo | | display_infos | bool | False | Display additonal information about ISO codes and the NLU namespace structure.|
| n_jobs | Optional[int] | 3| False | How many cores to use for paralellzing when using Sklearn Dimension Reduction algorithms. |

Supported Manifold Algorithms for Word, Sentence, and Entity Embeddings

Supported Matrix Decomposition Algorithms for Word, Sentence and Entity Embeddings

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