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Sparknlp-1174 Adding Partitioning Documents Feature #14579
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Merged
DevinTDHa merged 20 commits into release/602-release-candidate from feature/SPARKNLP-1174-Adding-PartitionTransformer May 26, 2025
Merged
Sparknlp-1174 Adding Partitioning Documents Feature #14579
DevinTDHa merged 20 commits into release/602-release-candidate from feature/SPARKNLP-1174-Adding-PartitionTransformer May 26, 2025
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…aks and infer table options
…tch Python convention
DevinTDHa reviewed May 23, 2025
* Update conda meta.yaml for 6.0.1 [skip test] * added documentation to file readers * updated docs for partition class in scala and python * fixed typos in sparkNLPReader and added documentation for Partition class * added parameters to Partition class and updated read function docs in Readers * updated readers documentation with ipynb path * updated partition description * fixed errors in email readers * fixed errors in email readers * added docs for partition transformer and pdf reader * added docs for python partition transformer and pdf reader * added docs for python partition transformer and pdf reader * updated docs to render partition and reader * reverted changes in init.py * reverted changes in imports * updated formatting docs for pdf reader * updated formatting of docs for spark nlp reader * updated formatting of docs for partition * updated formatting of docs for partition_transformer * updating links to notebooks and partition transformer description --------- Co-authored-by: Devin Ha <devin@trungducha.de> Co-authored-by: Paulami Bhattacharya <paulamibhattacharya@Paulamis-MacBook-Pro.local>
DevinTDHa approved these changes May 26, 2025
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Description
This PR introduces the new
Partitioncomponent andPartitionTransformerannotator to spark-nlp, providing a streamlined and user-friendly interface for interacting with Spark NLP readers. It also extends the configurability of these readers by adding support for additional options.Key Improvements:
Motivation and Context
The current approach to interfacing with Spark NLP readers is fragmented and lacks flexibility, often requiring custom code for handling various input sources and options. This makes onboarding harder for new users and hinders reuse across pipelines.
The Partitioning component abstracts these complexities by:
How Has This Been Tested?
Screenshots (if appropriate):
Types of changes
Checklist: