bump the version to 5.2.0 [skip test]

README.md

@@ -172,7 +172,7 @@ To use Spark NLP you need the following requirements:
 
 **GPU (optional):**
 
-Spark NLP 5.1.4 is built with ONNX 1.15.1 and TensorFlow 2.7.1 deep learning engines. The minimum following NVIDIA® software are only required for GPU support:
+Spark NLP 5.2.0 is built with ONNX 1.15.1 and TensorFlow 2.7.1 deep learning engines. The minimum following NVIDIA® software are only required for GPU support:
 
 - NVIDIA® GPU drivers version 450.80.02 or higher
 - CUDA® Toolkit 11.2
@@ -188,7 +188,7 @@ $ java -version
 $ conda create -n sparknlp python=3.7 -y
 $ conda activate sparknlp
 # spark-nlp by default is based on pyspark 3.x
-$ pip install spark-nlp==5.1.4 pyspark==3.3.1
+$ pip install spark-nlp==5.2.0 pyspark==3.3.1
 ```
 
 In Python console or Jupyter `Python3` kernel:
@@ -233,7 +233,7 @@ For more examples, you can visit our dedicated [examples](https://github.com/Joh
 
 ## Apache Spark Support
 
-Spark NLP *5.1.4* 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
+Spark NLP *5.2.0* 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
 
 | 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 |
 |-----------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|
@@ -273,7 +273,7 @@ Find out more about `Spark NLP` versions from our [release notes](https://github
 
 ## Databricks Support
 
-Spark NLP 5.1.4 has been tested and is compatible with the following runtimes:
+Spark NLP 5.2.0 has been tested and is compatible with the following runtimes:
 
 **CPU:**
 
@@ -340,7 +340,7 @@ Spark NLP 5.1.4 has been tested and is compatible with the following runtimes:
 
 ## EMR Support
 
-Spark NLP 5.1.4 has been tested and is compatible with the following EMR releases:
+Spark NLP 5.2.0 has been tested and is compatible with the following EMR releases:
 
 - emr-6.2.0
 - emr-6.3.0
@@ -387,11 +387,11 @@ Spark NLP supports all major releases of Apache Spark 3.0.x, Apache Spark 3.1.x,
 ```sh
 # CPU
 
-spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.4
+spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.2.0
 
-pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.4
+pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.2.0
 
-spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.4
+spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.2.0
 ```
 
 The `spark-nlp` has been published to
@@ -400,11 +400,11 @@ the [Maven Repository](https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/s
 ```sh
 # GPU
 
-spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.1.4
+spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.2.0
 
-pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.1.4
+pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.2.0
 
-spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.1.4
+spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.2.0
 
 ```
 
@@ -414,11 +414,11 @@ the [Maven Repository](https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/s
 ```sh
 # AArch64
 
-spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.1.4
+spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.2.0
 
-pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.1.4
+pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.2.0
 
-spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.1.4
+spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.2.0
 
 ```
 
@@ -428,11 +428,11 @@ the [Maven Repository](https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/s
 ```sh
 # M1/M2 (Apple Silicon)
 
-spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.1.4
+spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.2.0
 
-pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.1.4
+pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.2.0
 
-spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.1.4
+spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.2.0
 
 ```
 
@@ -446,7 +446,7 @@ set in your SparkSession:
 spark-shell \
   --driver-memory 16g \
   --conf spark.kryoserializer.buffer.max=2000M \
-  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.4
+  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.2.0
 ```
 
 ## Scala
@@ -464,7 +464,7 @@ coordinates:
 <dependency>
     <groupId>com.johnsnowlabs.nlp</groupId>
     <artifactId>spark-nlp_2.12</artifactId>
-    <version>5.1.4</version>
+    <version>5.2.0</version>
 </dependency>
 ```
 
@@ -475,7 +475,7 @@ coordinates:
 <dependency>
     <groupId>com.johnsnowlabs.nlp</groupId>
     <artifactId>spark-nlp-gpu_2.12</artifactId>
-    <version>5.1.4</version>
+    <version>5.2.0</version>
 </dependency>
 ```
 
@@ -486,7 +486,7 @@ coordinates:
 <dependency>
     <groupId>com.johnsnowlabs.nlp</groupId>
     <artifactId>spark-nlp-aarch64_2.12</artifactId>
-    <version>5.1.4</version>
+    <version>5.2.0</version>
 </dependency>
 ```
 
@@ -497,7 +497,7 @@ coordinates:
 <dependency>
     <groupId>com.johnsnowlabs.nlp</groupId>
     <artifactId>spark-nlp-silicon_2.12</artifactId>
-    <version>5.1.4</version>
+    <version>5.2.0</version>
 </dependency>
 ```
 
@@ -507,28 +507,28 @@ coordinates:
 
 ```sbtshell
 // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp
-libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "5.1.4"
+libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "5.2.0"
 ```
 
 **spark-nlp-gpu:**
 
 ```sbtshell
 // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-gpu
-libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu" % "5.1.4"
+libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu" % "5.2.0"
 ```
 
 **spark-nlp-aarch64:**
 
 ```sbtshell
 // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-aarch64
-libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-aarch64" % "5.1.4"
+libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-aarch64" % "5.2.0"
 ```
 
 **spark-nlp-silicon:**
 
 ```sbtshell
 // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-silicon
-libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-silicon" % "5.1.4"
+libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-silicon" % "5.2.0"
 ```
 
 Maven
@@ -550,7 +550,7 @@ If you installed pyspark through pip/conda, you can install `spark-nlp` through
 Pip:
 
 ```bash
-pip install spark-nlp==5.1.4
+pip install spark-nlp==5.2.0
 ```
 
 Conda:
@@ -579,7 +579,7 @@ spark = SparkSession.builder
     .config("spark.driver.memory", "16G")
     .config("spark.driver.maxResultSize", "0")
     .config("spark.kryoserializer.buffer.max", "2000M")
-    .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.4")
+    .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:5.2.0")
     .getOrCreate()
 ```
 
@@ -650,7 +650,7 @@ Use either one of the following options
 - Add the following Maven Coordinates to the interpreter's library list
 
 ```bash
-com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.4
+com.johnsnowlabs.nlp:spark-nlp_2.12:5.2.0
 ```
 
 - Add a path to pre-built jar from [here](#compiled-jars) in the interpreter's library list making sure the jar is
@@ -661,7 +661,7 @@ com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.4
 Apart from the previous step, install the python module through pip
 
 ```bash
-pip install spark-nlp==5.1.4
+pip install spark-nlp==5.2.0
 ```
 
 Or you can install `spark-nlp` from inside Zeppelin by using Conda:
@@ -689,7 +689,7 @@ launch the Jupyter from the same Python environment:
 $ conda create -n sparknlp python=3.8 -y
 $ conda activate sparknlp
 # spark-nlp by default is based on pyspark 3.x
-$ pip install spark-nlp==5.1.4 pyspark==3.3.1 jupyter
+$ pip install spark-nlp==5.2.0 pyspark==3.3.1 jupyter
 $ jupyter notebook
 ```
 
@@ -706,7 +706,7 @@ export PYSPARK_PYTHON=python3
 export PYSPARK_DRIVER_PYTHON=jupyter
 export PYSPARK_DRIVER_PYTHON_OPTS=notebook
 
-pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.4
+pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.2.0
 ```
 
 Alternatively, you can mix in using `--jars` option for pyspark + `pip install spark-nlp`
@@ -733,7 +733,7 @@ This script comes with the two options to define `pyspark` and `spark-nlp` versi
 # -s is for spark-nlp
 # -g will enable upgrading libcudnn8 to 8.1.0 on Google Colab for GPU usage
 # by default they are set to the latest
-!wget https://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.3 -s 5.1.4
+!wget https://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.3 -s 5.2.0
 ```
 
 [Spark NLP quick start on Google Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/quick_start_google_colab.ipynb)
@@ -756,7 +756,7 @@ This script comes with the two options to define `pyspark` and `spark-nlp` versi
 # -s is for spark-nlp
 # -g will enable upgrading libcudnn8 to 8.1.0 on Kaggle for GPU usage
 # by default they are set to the latest
-!wget https://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.3 -s 5.1.4
+!wget https://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.3 -s 5.2.0
 ```
 
 [Spark NLP quick start on Kaggle Kernel](https://www.kaggle.com/mozzie/spark-nlp-named-entity-recognition) is a live
@@ -775,9 +775,9 @@ demo on Kaggle Kernel that performs named entity recognitions by using Spark NLP
 
 3. In `Libraries` tab inside your cluster you need to follow these steps:
 
-   3.1. Install New -> PyPI -> `spark-nlp==5.1.4` -> Install
+   3.1. Install New -> PyPI -> `spark-nlp==5.2.0` -> Install
 
-   3.2. Install New -> Maven -> Coordinates -> `com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.4` -> Install
+   3.2. Install New -> Maven -> Coordinates -> `com.johnsnowlabs.nlp:spark-nlp_2.12:5.2.0` -> Install
 
 4. Now you can attach your notebook to the cluster and use Spark NLP!
 
@@ -828,7 +828,7 @@ A sample of your software configuration in JSON on S3 (must be public access):
       "spark.kryoserializer.buffer.max": "2000M",
       "spark.serializer": "org.apache.spark.serializer.KryoSerializer",
       "spark.driver.maxResultSize": "0",
-      "spark.jars.packages": "com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.4"
+      "spark.jars.packages": "com.johnsnowlabs.nlp:spark-nlp_2.12:5.2.0"
     }
 }]
 ```
@@ -837,7 +837,7 @@ A sample of AWS CLI to launch EMR cluster:
 
 ```.sh
 aws emr create-cluster \
---name "Spark NLP 5.1.4" \
+--name "Spark NLP 5.2.0" \
 --release-label emr-6.2.0 \
 --applications Name=Hadoop Name=Spark Name=Hive \
 --instance-type m4.4xlarge \
@@ -901,7 +901,7 @@ gcloud dataproc clusters create ${CLUSTER_NAME} \
   --enable-component-gateway \
   --metadata 'PIP_PACKAGES=spark-nlp spark-nlp-display google-cloud-bigquery google-cloud-storage' \
   --initialization-actions gs://goog-dataproc-initialization-actions-${REGION}/python/pip-install.sh \
-  --properties spark:spark.serializer=org.apache.spark.serializer.KryoSerializer,spark:spark.driver.maxResultSize=0,spark:spark.kryoserializer.buffer.max=2000M,spark:spark.jars.packages=com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.4
+  --properties spark:spark.serializer=org.apache.spark.serializer.KryoSerializer,spark:spark.driver.maxResultSize=0,spark:spark.kryoserializer.buffer.max=2000M,spark:spark.jars.packages=com.johnsnowlabs.nlp:spark-nlp_2.12:5.2.0
 ```
 
 2. On an existing one, you need to install spark-nlp and spark-nlp-display packages from PyPI.
@@ -940,7 +940,7 @@ spark = SparkSession.builder
     .config("spark.kryoserializer.buffer.max", "2000m")
     .config("spark.jsl.settings.pretrained.cache_folder", "sample_data/pretrained")
     .config("spark.jsl.settings.storage.cluster_tmp_dir", "sample_data/storage")
-    .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.4")
+    .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:5.2.0")
     .getOrCreate()
 ```
 
@@ -954,7 +954,7 @@ spark-shell \
   --conf spark.kryoserializer.buffer.max=2000M \
   --conf spark.jsl.settings.pretrained.cache_folder="sample_data/pretrained" \
   --conf spark.jsl.settings.storage.cluster_tmp_dir="sample_data/storage" \
-  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.4
+  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.2.0
 ```
 
 **pyspark:**
@@ -967,7 +967,7 @@ pyspark \
   --conf spark.kryoserializer.buffer.max=2000M \
   --conf spark.jsl.settings.pretrained.cache_folder="sample_data/pretrained" \
   --conf spark.jsl.settings.storage.cluster_tmp_dir="sample_data/storage" \
-  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.4
+  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.2.0
 ```
 
 **Databricks:**
@@ -1239,7 +1239,7 @@ spark = SparkSession.builder
     .config("spark.driver.memory", "16G")
     .config("spark.driver.maxResultSize", "0")
     .config("spark.kryoserializer.buffer.max", "2000M")
-    .config("spark.jars", "/tmp/spark-nlp-assembly-5.1.4.jar")
+    .config("spark.jars", "/tmp/spark-nlp-assembly-5.2.0.jar")
     .getOrCreate()
 ```
 
@@ -1248,7 +1248,7 @@ spark = SparkSession.builder
   version (3.0.x, 3.1.x, 3.2.x, 3.3.x, 3.4.x, and 3.5.x)
 - If you are local, you can load the Fat JAR from your local FileSystem, however, if you are in a cluster setup you need
   to put the Fat JAR on a distributed FileSystem such as HDFS, DBFS, S3, etc. (
-  i.e., `hdfs:///tmp/spark-nlp-assembly-5.1.4.jar`)
+  i.e., `hdfs:///tmp/spark-nlp-assembly-5.2.0.jar`)
 
 Example of using pretrained Models and Pipelines in offline:
 

build.sbt

@@ -6,7 +6,7 @@ name := getPackageName(is_silicon, is_gpu, is_aarch64)
 
 organization := "com.johnsnowlabs.nlp"
 
-version := "5.1.4"
+version := "5.2.0"
 
 (ThisBuild / scalaVersion) := scalaVer
 

docs/_layouts/landing.html

@@ -201,7 +201,7 @@ <h3 class="grey h3_title">{{ _section.title }}</h3>
                   <div class="highlight-box">
     {% highlight bash %}
     # Using PyPI
-    $ pip install spark-nlp==5.1.4
+    $ pip install spark-nlp==5.2.0
 
     # Using Anaconda/Conda
     $ conda install -c johnsnowlabs spark-nlp

docs/en/concepts.md

@@ -66,7 +66,7 @@ $ java -version
 $ conda create -n sparknlp python=3.7 -y
 $ conda activate sparknlp
 # spark-nlp by default is based on pyspark 3.x
-$ pip install spark-nlp==5.1.4 pyspark==3.3.1 jupyter
+$ pip install spark-nlp==5.2.0 pyspark==3.3.1 jupyter
 $ jupyter notebook
 ```
 

docs/en/examples.md

@@ -18,7 +18,7 @@ $ java -version
 # should be Java 8 (Oracle or OpenJDK)
 $ conda create -n sparknlp python=3.7 -y
 $ conda activate sparknlp
-$ pip install spark-nlp==5.1.4 pyspark==3.3.1
+$ pip install spark-nlp==5.2.0 pyspark==3.3.1
 ```
 
 </div><div class="h3-box" markdown="1">
@@ -40,7 +40,7 @@ This script comes with the two options to define `pyspark` and `spark-nlp` versi
 # -p is for pyspark
 # -s is for spark-nlp
 # by default they are set to the latest
-!bash colab.sh -p 3.2.3 -s 5.1.4
+!bash colab.sh -p 3.2.3 -s 5.2.0
 ```
 
 [Spark NLP quick start on Google Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/quick_start_google_colab.ipynb) is a live demo on Google Colab that performs named entity recognitions and sentiment analysis by using Spark NLP pretrained pipelines.

docs/en/hardware_acceleration.md

@@ -49,7 +49,7 @@ Since the new Transformer models such as BERT for Word and Sentence embeddings a
 | DeBERTa Large     |        +477%(5.8x)        |
 | Longformer Base   |         +52%(1.5x)        |
 
-Spark NLP 5.1.4 is built with TensorFlow 2.7.1 and the following NVIDIA® software are only required for GPU support:
+Spark NLP 5.2.0 is built with TensorFlow 2.7.1 and the following NVIDIA® software are only required for GPU support:
 
 - NVIDIA® GPU drivers version 450.80.02 or higher
 - CUDA® Toolkit 11.2