Sparknlp

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Overview
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This release is huge! Spark-NLP made the leap into Spark 2.4.0, even with the challenge of not having everyone yet on board there (i.e. Zeppelin doesn't yet support it).
In this version we release three new NLP annotators. Two for dependency parsing processes and one for contextual deep learning based spell checking.
We also significantly improved OCR functionality, fine-tuning capabilities and general output performance, particularly on tesseract.
Finally, there's plenty of bug fixes and improvements in the word embeddings field, along with performance boosts and reduced disk IO.
Feel free to shoot us with any feedback you have! Particularly on your Spark 2.4.x experience.

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New Features
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* Built on top of Spark 2.4.0
* Dependency Parser annotator allows for sentence relationship encoding
* Typed Dependency Parser annotator allows for labeling relationships within dependency tags
* ContextSpellChecker is our first Deep Learning based Spell Checker that evaluates context and not only tokens

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Enhancements
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* More OCR parameters exposed for further fine tuning, including preferred methods priority and page segmentation modes
* OCR now has a setting setSplitPages() which allows setting whether to output one page per row or the entire document instead
* Improved word embeddings performance when working in local filesystems
* Reduced the amount of disk IO when working with Word Embeddings
* All python notebooks improved for better readability and better documentation
* Simplified PySpark interface API
* CoNLLGenerator utility class which helps building CoNLL-2003 files for NER training
* EmbeddingsHelper now allows reading word embeddings files directly from s3a:// paths

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Bugfixes
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* Solved race-condition issues in regards of cluster usage of RocksDB index for embeddings
* Fixed application.conf reading bug which didn't properly refresh AWS credentials
* RocksDB index no longer uses compression, in order to support Windows without native RocksDB compression libraries
* Solved various python default parameter settings
* Fixed circular dependency with jbig pdfbox image OCR

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Deprecations
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* DeIdentification annotator is no longer supported in the open source version of Spark-NLP
* AssertionStatus annotator is no longer supported in the open source version of Spark-NLP

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Overview
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This hotfix release focuses on fixing word-embeddings cluster problems on some frameworks such as Databricsk, while keeping 1.7.x performance benefits. Various YARN based clusters have been tested, databricks cloud among them to test this hotfix.
Aside of that, multiple improvements have been commited towards a better support of PySpark-NLP, fixing diverse technical issues in the API that help consistency in Annotator's super classes.
Finally, PIP installation has been made easier with a SparkNLP class that creates SparkSession automatically, for those who are learning Python Spark on their local computers.
Thanks to all the community for reporting issues.

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Bugfixes
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* Fixed 'RocksDB not serializable' when running LightPipeline scenarios or using _.functions implicits
* Fixed dependency with apache.commons.codec causing Apache Zeppelin 0.8.0 not to work in %pyspark
* Fixed Python pretrained() downloader not correctly setting Params and incorrectly creating new Model UIDs
* Fixed error 'JavaPackage not callable' when using AnnotatorModel.load() API without instantiating the class first
* Fixed Spark addFiles missing local file causing Word Embeddings not properly work in some Cluster-based frameworks
* Fixed broadcast NoSuchElementException `Failed to get broadcast_6_piece0 of broadcast_6` causing pretrained models not work in cluster frameworks (thanks EnricoMi)

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Developer API
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* EmbeddingsHelper.setRef() has been removed. Reference is now set implicitly through EmbeddingsHelper.load(). Does not need to be loaded before deserializing models.
* Fixed and properly renamed chunk2doc and dock2chunk transformers, should now be working as expected
* Renamed setCompositeTokens to setCompositeTokensPatterns to help user remind that regex are being used in such Param
* Fixed PySpark automatic getter and setter Param generation when using pretrained() or load() models
* Simplified cluster path resolution for word embeddings

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Other
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* sparknlp.base now contains SparkNLP() classs which automatically cretes SparkSession using appropriate jar settings. Helps newcomers get started in PySpark NLP.

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Overview
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Quick release with another hotfix, due to a new found bug when deserializing word embeddings in a distributed fs. Also introduces changes in application.conf reader in order
to allow run-time changes. Also introduces renaming from EmbeddingsHelper API.

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Bugfixes
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* Fixed embeddings deserialization from distributed filesystem (caused due to windows pathfix)
* Fixed application.conf not reading changes in runtime
* Added missing remote_locs argument in python pretrained() functions
* Fixed wrong build version introduced in 1.7.1 to detect proper pretrained models version

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Developer API
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* Renamed EmbeddingsHelper functions for more convenience

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Overview
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Thanks to our slack community (Bryan Wilkinson, maziyarpanahi, apiltamang), a few bugs been pointed out very quickly from 1.7.0 release. This hotfix fixes an embeddings deserialization issue when cache_pretrained is located on a distributed filesystem.
Also, fixes some path resolution in Windows OS. Thanks to Maziyar, .gitattributes been added in order to identify proper languages in GitHub.
Finally, 1.7.1 adds a missing annotator from 1.7.0 Chunk2Doc, which converts CHUNK types into DOCUMENT types, for further retokenization or other annotations.

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Enhancements
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* Chunk2Doc annotator converts annotatorType from CHUNK to DOCUMENT

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Bugfixes
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* Fixed embedding-based annotators deserialization error when cache_pretrained is on distributed fs (Thanks Bryan Wilkinson for pointing out issue and testing fix)
* Fixed windows path reading when deserializing embeddings (Thanks apiltamang)

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Other
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* .gitattributes added in order to properly discard jupyter as main language for GitHub repo (thanks maziyarpanahi)

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Overview
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Having multiple annotators that use the same word embeddings set, may result in huge pipelines, driver memory and storage consumption.
Since now on, embeddings may be shared and reutilized across annotators making the process much more efficient.
Also, thanks to apiltamang, we now better support path resolution for Windows implementations.

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Enhancements
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Memory and storage saving by allowing annotators with embeddings through params 'includeEmbeddings' and 'embeddingsRef' to allow them to set whether they should be included when saved, or referenced by id from other annotators
EmbeddingsHelper class allows embeddings management

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Bug fixes
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Thanks to apiltamang for improving URI path support for Windows Servers

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Developer API
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Embeddings interfaces and method names completely refactored, hopefully simplified and easier to understand

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Overview
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This release includes a new annotator for de-identification of sensitive information. It uses CHUNK annotations, meaning its accuracy will depend on previous annotators on the pipeline.
Also, OCR capabilities have been improved in the OCR module.
In terms of broken stuff, we've fixed a few annoying bugs on SymmetricDelete and SentenceDetector explode feature.
Finally, pip is now part of the official repositories, meaning you can install it just as any other module. It also includes jars and we've added a SparkNLP class which creates SparkSession easily for you.
Thanks again for all community contribution in issues, feedback and comments in GitHub and in Slack.

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New features
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* DeIdentification annotator, takes DOCUMENT and TOKEN from the original sentence, plus a CHUNK annotation to anonymize target chunk in sentence. CHUNK annotation might come from NerConverter, TextMatcher or other chunk annotators.

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Enhancements
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* Kernel zoom and region erosion improve overall detection quality. Fixed some stability bugs. Improved parallelism

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Bug fixes
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* Sentence Detector explode sentences into rows now works properly
* Fixed Dictionary-based sentiment detector not working on pyspark
* Added missing NerConverter to annotator._ imports
* Fixed SymmetricDelete spell checker deleting tokens in some scenarios
* Fixed SymmetricDelete spell checker unwilling lower-casing

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Other
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* PySpark pip now part from official pip repos
* Pip installation now includes corresponding spark-nlp jar. base module includes SparkNLP SparkSession creator

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