Spark-nlp

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Overview
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This minor release fixes a few bugs in some of our annotators reported by our community.
As always, we thank our community for their feedback and questions in our Slack channel.

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Bugfixes
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* Fix UniversalSentenceEncoder.pretrained() that failed in Python
* Fix ElmoEmbeddings.pretrained() that failed in Python
* Fix ElmoEmbeddings poolingLayer param to be a string as expected
* Fix ChunkEmbeddings to preserve chunk's index
* Fix NGramGenerator and missing chunk metadata

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New Features
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* Add GPU support param in Spark NLP start function: sparknlp.start(gpu=true)
* Improve create_model.py to create custom TF graph for NerDLApproach

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Documentation
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* Update documentation for release of Spark NLP 2.4.x
* Update the entire [spark-nlp-workshop](https://github.com/JohnSnowLabs/spark-nlp-models) notebooks for Spark NLP 2.4.x
* Update the entire [spark-nlp-models](https://github.com/JohnSnowLabs/spark-nlp-workshop) repository with new pre-trained models and pipelines

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========
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Overview
---------------
This minor release fixes a few bugs in some of our annotators reported by our community.
As always, we thank our community for their feedback and questions in our Slack channel.

----------------
Bugfixes
----------------
* Improve ChunkEmbeddings annotator and fix the empty chunk result
* Fix UniversalSentenceEncoder crashing on empty Tensor
* Fix NorvigSweetingModel missing sentenceId that results in NGramsGenerator crashing
* Fix missing storageRef in embeddings' column for ElmoEmbeddings annotator

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Documentation
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* Update documentation for release of Spark NLP 2.4.x
* Add new features such as ElmoEmbeddings and UniversalSentenceEncoder
* Add multiple programming languages for demos and examples
* Update the entire [spark-nlp-models](https://github.com/JohnSnowLabs/spark-nlp-models) repository with new pre-trained models and pipelines

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Bugfixes
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* https://github.com/JohnSnowLabs/spark-nlp/pull/702 Date matcher fixes flexible dates
* https://github.com/JohnSnowLabs/spark-nlp/pull/718 Fixed a bug in a pragmatic sentence detector where a sub matched group contained a dollar sign.
* https://github.com/JohnSnowLabs/spark-nlp/pull/719 Move import to top-level to avoid import fail in Spark NLP functions
* https://github.com/JohnSnowLabs/spark-nlp/pull/709 https://github.com/JohnSnowLabs/spark-nlp/pull/716 Some improvements in our documentation thanks to marcinic howmuchcomputer

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Overview
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This minor release fixes a bug in ChunkEmbeddings causing an out of boundaries exception in some scenarios. We
also switch to maven coordinates as default source for start() function since spark-packages has not been responsive
on their package approval process. Thank you all for your consistent feedback.

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Bugfixes
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* Fixed a bug in Chunk Embeddings caused by out of bound exception in some scenarios

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Other
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* start() function switched to use maven coordinates instead

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Overview
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We would like to thank you all for your valuable feedback via our Slack channels and our GitHub repositories.

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Overview
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Thank you, as always, for the feedback given at Slack and our repos. The most important part of this release,
is how we internally started organizing models. We'll be deploying our model news in
https://github.com/JohnSnowLabs/spark-nlp-models . The models repo will be kept up to date.

As for this release, it improves various internal API functionalities, allowing for positive side-effects across
the library. As an important enhancement, we have added user UDFs and functions for both Scala and Python users
to be able to easily manipulate annotations on DataFrames. Finally, we have fixed various bugs in embeddings
metadata to make sure we provide accurate offsetting information for other annotators to consume it successfully.

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Enhancements
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* Revamped functions in Scala and python to help users deal with annotations from dataframes or in UDF form, such as `map_annotations` and `filter_by_annotations`

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Bugfixes
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* Fixed bugs in ChunkEmbeddings and SentenceEmbeddings causing them to report wrong metadata and offset values
* Fixed a nested import issue in Python causing LightPipelines not to work in some environments

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Developer API
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* downloadModel is now flexible as to which inner downloader class is being used to access AnnotatorModel reference
* pretrained API now deals with defaultModelName as an Option to allow non default pretrained models

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Other
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* version() now returns the version string instead of just printing it

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