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 Decompositionalgorithms to reduce the dimensionality of Word Embeddingsto 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', ''] 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