Visualization package for Spark NLP
Project description
spark-nlp-display
A library for the simple visualization of different types of Spark NLP annotations.
Supported Visualizations:
- Dependency Parser
- Named Entity Recognition
- Entity Resolution
- Relation Extraction
- Assertion Status
Complete Tutorial
https://github.com/JohnSnowLabs/spark-nlp-display/blob/main/tutorials/Spark_NLP_Display.ipynb
Requirements
- spark-nlp
- ipython
- svgwrite
- pandas
- numpy
Installation
pip install spark-nlp-display
How to use
Dependency Parser
from sparknlp_display import DependencyParserVisualizer
dependency_vis = DependencyParserVisualizer()
dependency_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe.
pos_col = 'pos', #specify the pos column
dependency_col = 'dependency', #specify the dependency column
dependency_type_col = 'dependency_type' #specify the dependency type column
)
Named Entity Recognition
from sparknlp_display import NerVisualizer
ner_vis = NerVisualizer()
ner_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe
label_col='entities', #specify the entity column
document_col='document' #specify the document column (default: 'document')
labels=['PER'] #only allow these labels to be displayed. (default: [] - all labels will be displayed)
)
## To set custom label colors:
ner_vis.set_label_colors({'LOC':'#800080', 'PER':'#77b5fe'}) #set label colors by specifying hex codes
Entity Resolution
from sparknlp_display import EntityResolverVisualizer
er_vis = EntityResolverVisualizer()
er_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe
label_col='entities', #specify the ner result column
resolution_col = 'resolution'
document_col='document' #specify the document column (default: 'document')
)
## To set custom label colors:
er_vis.set_label_colors({'TREATMENT':'#800080', 'PROBLEM':'#77b5fe'}) #set label colors by specifying hex codes
Relation Extraction
from sparknlp_display import RelationExtractionVisualizer
re_vis = RelationExtractionVisualizer()
re_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe
relation_col = 'relations', #specify relations column
document_col = 'document', #specify document column
show_relations=True #display relation names on arrows (default: True)
)
Assertion Status
from sparknlp_display import AssertionVisualizer
assertion_vis = AssertionVisualizer()
assertion_vis.display(pipeline_result[0],
label_col = 'entities', #specify the ner result column
assertion_col = 'assertion' #specify assertion column
document_col = 'document' #specify the document column (default: 'document')
)
## To set custom label colors:
assertion_vis.set_label_colors({'TREATMENT':'#008080', 'problem':'#800080'}) #set label colors by specifying hex codes
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
spark-nlp-display-1.1.tar.gz
(79.4 kB
view hashes)
Built Distribution
Close
Hashes for spark_nlp_display-1.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2d08d5fc05222f5cb9313550c023e5521f7bc32a747d44c13a3b329d22b3c454 |
|
MD5 | 3f318a9f69d1b6a295ea2b8216fddbd0 |
|
BLAKE2b-256 | 6f8c21d3a78b2f46a112e1d5215360c29760e7b34f383161b2288ffc7c95d834 |