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Merge pull request #14511 from JohnSnowLabs/release/553-release-candidate
Release/553 release candidate
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‎CHANGELOG

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========
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5.5.3
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========
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----------------
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Bug Fixes & Enhancements
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----------------
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* BGEEmbeddings: The default pretrained model for BGEEmbeddings has been changed from "bge_base" to "bge_small_en_v1.5". Users relying on the old default will need to explicitly specify "bge_base" in the pretrained method.
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* Added useCLSToken parameter to allow users to choose between CLS token pooling and attention-based average pooling for sentence embeddings.
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* BGEEmbeddings: BGEEmbeddings now supports a `useCLSToken` parameter, which defaults to True. This affects the embedding calculation strategy. Existing users should verify their usage and potentially set this parameter explicitly.
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* Added HasClsTokenProperties in Scala: Introduced the HasClsTokenProperties trait in Scala providing useCLSToken parameter functionality for relevant annotators.
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* Fixing wrong padding in attention mask in `MPNet`, `BGE`, `E5`, `Mxbai`, `Nomic`, `SnowFlake`, and `UAE`. This resulted in wrong inference results in some cases and not equal to the ONNX version in transformers/sentence-transformers.
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* Various Performance Optimizations: Multiple changes across different models (Albert, Bart, CLIP, CamemBert, ConvNextClassifier, DeBerta, DistilBert, E5, Instructor, MPNet, Mxbai, Nomic, RoBerta, SnowFlake, UAE, ViTClassifier, VisionEncoderDecoder, Wav2Vec2, XlmRoBertaClassification, XlmRoberta) appear to focus on performance improvements and code cleanup, especially related to OpenVINO and ONNX inference. These may lead to faster inference times.
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* Fixing Llama3 download issue in Python.
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========
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5.5.2
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========

‎README.md

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$ conda create -n sparknlp python=3.7 -y
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$ conda activate sparknlp
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# spark-nlp by default is based on pyspark 3.x
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$ pip install spark-nlp==5.5.2 pyspark==3.3.1
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$ pip install spark-nlp==5.5.3 pyspark==3.3.1
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```
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In Python console or Jupyter `Python3` kernel:
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### Apache Spark Support
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Spark NLP *5.5.2* has been built on top of Apache Spark 3.4 while fully supports Apache Spark 3.0.x, 3.1.x, 3.2.x, 3.3.x, 3.4.x, and 3.5.x
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Spark NLP *5.5.3* has been built on top of Apache Spark 3.4 while fully supports Apache Spark 3.0.x, 3.1.x, 3.2.x, 3.3.x, 3.4.x, and 3.5.x
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| Spark NLP | Apache Spark 3.5.x | Apache Spark 3.4.x | Apache Spark 3.3.x | Apache Spark 3.2.x | Apache Spark 3.1.x | Apache Spark 3.0.x | Apache Spark 2.4.x | Apache Spark 2.3.x |
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|-----------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|
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### Databricks Support
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Spark NLP 5.5.2 has been tested and is compatible with the following runtimes:
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Spark NLP 5.5.3 has been tested and is compatible with the following runtimes:
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| **CPU** | **GPU** |
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|--------------------|--------------------|
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### EMR Support
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Spark NLP 5.5.2 has been tested and is compatible with the following EMR releases:
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Spark NLP 5.5.3 has been tested and is compatible with the following EMR releases:
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| **EMR Release** |
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|--------------------|
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from our official documentation.
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If you are interested, there is a simple SBT project for Spark NLP to guide you on how to use it in your
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projects [Spark NLP SBT S5.5.2r](https://github.com/maziyarpanahi/spark-nlp-starter)
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projects [Spark NLP SBT S5.5.3r](https://github.com/maziyarpanahi/spark-nlp-starter)
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### Python
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Please check [these instructions](https://sparknlp.org/docs/en/install#s3-integration) from our official documentation.
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## Document5.5.2
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## Document5.5.3
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### Examples
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keywords = {Spark, Natural language processing, Deep learning, Tensorflow, Cluster},
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abstract = {Spark NLP is a Natural Language Processing (NLP) library built on top of Apache Spark ML. It provides simple, performant & accurate NLP annotations for machine learning pipelines that can scale easily in a distributed environment. Spark NLP comes with 1100+ pretrained pipelines and models in more than 192+ languages. It supports nearly all the NLP tasks and modules that can be used seamlessly in a cluster. Downloaded more than 2.7 million times and experiencing 9x growth since January 2020, Spark NLP is used by 54% of healthcare organizations as the world’s most widely used NLP library in the enterprise.}
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}5.5.2
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}5.5.3
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```
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## Community support

‎build.sbt

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(ThisBuild / scalaVersion) := scalaVer
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# Using PyPI
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$ pip install spark-nlp==5.5.3
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# Using Anaconda/Conda
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$ conda install -c johnsnowlabs spark-nlp

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