Powered by [MPNet](https://sparknlp.org/docs/en/transformers#mpnetforsequenceclassification)
|Language|nlp.load() reference|Spark NLP Model reference|
|---|---|---|
|en|en.classify.mpnet.ukr_message|[mpnet_sequence_classifier_ukr_message](https://sparknlp.org/2024/01/10/mpnet_sequence_classifier_ukr_message_en.html)|
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Pipeline Tracer
[Tutorial Notebook](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/healthcare/pipeline_parser/Parser.ipynb)
The PipelineTracer is now accessible on NLU pipelines which is a versatile class designed to trace and analyze the stages of a pipeline, offering in-depth insights into entities, assertions, deidentification, classification, and relationships. It also facilitates the creation of parser dictionaries for building a PipelineOutputParser. Key functions include printing the pipeline schema, creating parser dictionaries, and retrieving possible assertions, relations, and entities. Also, provide direct access to parser dictionaries and available pipeline schemas
Load a pipe
python
pipe = nlp.load("en.explain_doc.clinical_oncology.pipeline")
Get all assertions predictable with pipe
python
pipe.getPossibleAssertions()
>>> ['Past', 'Family', 'Absent', 'Hypothetical', 'Possible', 'Present']
Get all entities predictable with pipe
python
pipe.getPossibleEntities()
>>> ['Cycle_Number','Direction','Histological_Type', .... ]
Get all relation predictable with pipe
python
pipe.getPossibleRelations()
>>> ['is_size_of', 'is_date_of', 'is_location_of', 'is_finding_of']
Predict parsed with configs
python
column_maps = pipe.createParserDictionary()
column_maps.update({"document_identifier": "clinical_deidentification"})
pipe = nlp.load("en.explain_doc.clinical_oncology.pipeline")
res = pipe.predict(data,parser_output=True, parser_config=column_maps)
pd.json_normalize(res['result'][0]["entities"])
**Powered By**: [PipelineTracer](https://nlp.johnsnowlabs.com/licensed/api/com/johnsnowlabs/util/tracer/PipelineTracer.html)
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📖Additional NLU resources
* [140+ NLU Tutorials](https://nlp.johnsnowlabs.com/docs/en/jsl/notebooks)
* [Streamlit visualizations docs](https://nlp.johnsnowlabs.com/docs/en/jsl/streamlit_viz_examples)
* The complete list of all 20000+ models & pipelines in 300+ languages is available on [Models Hub](https://nlp.johnsnowlabs.com/models)
* [Spark NLP publications](https://medium.com/spark-nlp)
* [NLU documentation](https://nlp.johnsnowlabs.com/docs/en/jsl/install)
* [Discussions](https://github.com/JohnSnowLabs/spark-nlp/discussions) Engage with other community members, share ideas, and show off how you use Spark NLP and NLU!
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Installation
shell
pip install johnsnowlabs