Bump the version to 5.1.0 [skip test]

KshitizGIT · Aug 24, 2023 · 288e69f · 288e69f
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diff --git a/CHANGELOG b/CHANGELOG
@@ -1,5 +1,5 @@
 ========
-5.0.2
+5.1.0
 ========
 ----------------
 New Features & Enhancements

diff --git a/README.md b/README.md
@@ -167,7 +167,7 @@ To use Spark NLP you need the following requirements:
 
 **GPU (optional):**
 
-Spark NLP 5.0.2 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 5.1.0 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
@@ -183,7 +183,7 @@ $ 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==5.0.2 pyspark==3.3.1
+$ pip install spark-nlp==5.1.0 pyspark==3.3.1
 ```
 
 In Python console or Jupyter `Python3` kernel:
@@ -228,7 +228,7 @@ For more examples, you can visit our dedicated [examples](https://github.com/Joh
 
 ## Apache Spark Support
 
-Spark NLP *5.0.2* 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, and 3.4.x
+Spark NLP *5.1.0* 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, and 3.4.x
 
 | Spark NLP | Apache Spark 2.3.x | Apache Spark 2.4.x | Apache Spark 3.0.x | Apache Spark 3.1.x | Apache Spark 3.2.x | Apache Spark 3.3.x | Apache Spark 3.4.x |
 |-----------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|
@@ -267,7 +267,7 @@ Find out more about `Spark NLP` versions from our [release notes](https://github
 
 ## Databricks Support
 
-Spark NLP 5.0.2 has been tested and is compatible with the following runtimes:
+Spark NLP 5.1.0 has been tested and is compatible with the following runtimes:
 
 **CPU:**
 
@@ -303,6 +303,8 @@ Spark NLP 5.0.2 has been tested and is compatible with the following runtimes:
 - 13.1 ML
 - 13.2
 - 13.2 ML
+- 13.3
+- 13.3 ML
 
 **GPU:**
 
@@ -322,10 +324,11 @@ Spark NLP 5.0.2 has been tested and is compatible with the following runtimes:
 - 13.0 ML & GPU
 - 13.1 ML & GPU
 - 13.2 ML & GPU
+- 13.3 ML & GPU
 
 ## EMR Support
 
-Spark NLP 5.0.2 has been tested and is compatible with the following EMR releases:
+Spark NLP 5.1.0 has been tested and is compatible with the following EMR releases:
 
 - emr-6.2.0
 - emr-6.3.0
@@ -369,11 +372,11 @@ 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_2.12:5.0.2
+spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.0
 
-pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.0.2
+pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.0
 
-spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.0.2
+spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.0
 ```
 
 The `spark-nlp` has been published to
@@ -382,11 +385,11 @@ the [Maven Repository](https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/s
 ```sh
 # GPU
 
-spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.0.2
+spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.1.0
 
-pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.0.2
+pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.1.0
 
-spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.0.2
+spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.1.0
 
 ```
 
@@ -396,11 +399,11 @@ the [Maven Repository](https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/s
 ```sh
 # AArch64
 
-spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.0.2
+spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.1.0
 
-pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.0.2
+pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.1.0
 
-spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.0.2
+spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.1.0
 
 ```
 
@@ -410,11 +413,11 @@ 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_2.12:5.0.2
+spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.1.0
 
-pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.0.2
+pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.1.0
 
-spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.0.2
+spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.1.0
 
 ```
 
@@ -428,7 +431,7 @@ set in your SparkSession:
 spark-shell \
   --driver-memory 16g \
   --conf spark.kryoserializer.buffer.max=2000M \
-  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.0.2
+  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.0
 ```
 
 ## Scala
@@ -446,7 +449,7 @@ coordinates:
 <dependency>
     <groupId>com.johnsnowlabs.nlp</groupId>
     <artifactId>spark-nlp_2.12</artifactId>
-    <version>5.0.2</version>
+    <version>5.1.0</version>
 </dependency>
 ```
 
@@ -457,7 +460,7 @@ coordinates:
 <dependency>
     <groupId>com.johnsnowlabs.nlp</groupId>
     <artifactId>spark-nlp-gpu_2.12</artifactId>
-    <version>5.0.2</version>
+    <version>5.1.0</version>
 </dependency>
 ```
 
@@ -468,7 +471,7 @@ coordinates:
 <dependency>
     <groupId>com.johnsnowlabs.nlp</groupId>
     <artifactId>spark-nlp-aarch64_2.12</artifactId>
-    <version>5.0.2</version>
+    <version>5.1.0</version>
 </dependency>
 ```
 
@@ -479,7 +482,7 @@ coordinates:
 <dependency>
     <groupId>com.johnsnowlabs.nlp</groupId>
     <artifactId>spark-nlp-silicon_2.12</artifactId>
-    <version>5.0.2</version>
+    <version>5.1.0</version>
 </dependency>
 ```
 
@@ -489,28 +492,28 @@ coordinates:
 
 ```sbtshell
 // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp
-libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "5.0.2"
+libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "5.1.0"
 ```
 
 **spark-nlp-gpu:**
 
 ```sbtshell
 // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-gpu
-libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu" % "5.0.2"
+libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu" % "5.1.0"
 ```
 
 **spark-nlp-aarch64:**
 
 ```sbtshell
 // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-aarch64
-libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-aarch64" % "5.0.2"
+libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-aarch64" % "5.1.0"
 ```
 
 **spark-nlp-silicon:**
 
 ```sbtshell
 // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-silicon
-libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-silicon" % "5.0.2"
+libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-silicon" % "5.1.0"
 ```
 
 Maven
@@ -532,7 +535,7 @@ If you installed pyspark through pip/conda, you can install `spark-nlp` through
 Pip:
 
 ```bash
-pip install spark-nlp==5.0.2
+pip install spark-nlp==5.1.0
 ```
 
 Conda:
@@ -561,7 +564,7 @@ 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_2.12:5.0.2")
+    .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.0")
     .getOrCreate()
 ```
 
@@ -632,7 +635,7 @@ Use either one of the following options
 - Add the following Maven Coordinates to the interpreter's library list
 
 ```bash
-com.johnsnowlabs.nlp:spark-nlp_2.12:5.0.2
+com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.0
 ```
 
 - Add a path to pre-built jar from [here](#compiled-jars) in the interpreter's library list making sure the jar is
@@ -643,7 +646,7 @@ com.johnsnowlabs.nlp:spark-nlp_2.12:5.0.2
 Apart from the previous step, install the python module through pip
 
 ```bash
-pip install spark-nlp==5.0.2
+pip install spark-nlp==5.1.0
 ```
 
 Or you can install `spark-nlp` from inside Zeppelin by using Conda:
@@ -671,7 +674,7 @@ 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==5.0.2 pyspark==3.3.1 jupyter
+$ pip install spark-nlp==5.1.0 pyspark==3.3.1 jupyter
 $ jupyter notebook
 ```
 
@@ -688,7 +691,7 @@ export PYSPARK_PYTHON=python3
 export PYSPARK_DRIVER_PYTHON=jupyter
 export PYSPARK_DRIVER_PYTHON_OPTS=notebook
 
-pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.0.2
+pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.0
 ```
 
 Alternatively, you can mix in using `--jars` option for pyspark + `pip install spark-nlp`
@@ -715,7 +718,7 @@ 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 5.0.2
+!wget https://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.3 -s 5.1.0
 ```
 
 [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)
@@ -738,7 +741,7 @@ 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 5.0.2
+!wget https://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.3 -s 5.1.0
 ```
 
 [Spark NLP quick start on Kaggle Kernel](https://www.kaggle.com/mozzie/spark-nlp-named-entity-recognition) is a live
@@ -757,9 +760,9 @@ 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==5.0.2` -> Install
+   3.1. Install New -> PyPI -> `spark-nlp==5.1.0` -> Install
 
-   3.2. Install New -> Maven -> Coordinates -> `com.johnsnowlabs.nlp:spark-nlp_2.12:5.0.2` -> Install
+   3.2. Install New -> Maven -> Coordinates -> `com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.0` -> Install
 
 4. Now you can attach your notebook to the cluster and use Spark NLP!
 
@@ -810,7 +813,7 @@ 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_2.12:5.0.2"
+      "spark.jars.packages": "com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.0"
     }
 }]
 ```
@@ -819,7 +822,7 @@ A sample of AWS CLI to launch EMR cluster:
 
 ```.sh
 aws emr create-cluster \
---name "Spark NLP 5.0.2" \
+--name "Spark NLP 5.1.0" \
 --release-label emr-6.2.0 \
 --applications Name=Hadoop Name=Spark Name=Hive \
 --instance-type m4.4xlarge \
@@ -883,7 +886,7 @@ 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_2.12:5.0.2
+  --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_2.12:5.1.0
 ```
 
 2. On an existing one, you need to install spark-nlp and spark-nlp-display packages from PyPI.
@@ -922,7 +925,7 @@ 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_2.12:5.0.2")
+    .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.0")
     .getOrCreate()
 ```
 
@@ -936,7 +939,7 @@ 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_2.12:5.0.2
+  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.0
 ```
 
 **pyspark:**
@@ -949,7 +952,7 @@ 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_2.12:5.0.2
+  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.1.0
 ```
 
 **Databricks:**
@@ -1221,7 +1224,7 @@ 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-5.0.2.jar")
+    .config("spark.jars", "/tmp/spark-nlp-assembly-5.1.0.jar")
     .getOrCreate()
 ```
 
@@ -1230,7 +1233,7 @@ spark = SparkSession.builder
   version (3.0.x, 3.1.x, 3.2.x, 3.3.x, and 3.4.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-5.0.2.jar`)
+  i.e., `hdfs:///tmp/spark-nlp-assembly-5.1.0.jar`)
 
 Example of using pretrained Models and Pipelines in offline:
 

diff --git a/build.sbt b/build.sbt
@@ -6,7 +6,7 @@ name := getPackageName(is_silicon, is_gpu, is_aarch64)
 
 organization := "com.johnsnowlabs.nlp"
 
-version := "5.0.2"
+version := "5.1.0"
 
 (ThisBuild / scalaVersion) := scalaVer
 

diff --git a/docs/_layouts/landing.html b/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==5.0.2
+    $ pip install spark-nlp==5.1.0
 
     # Using Anaconda/Conda
     $ conda install -c johnsnowlabs spark-nlp

diff --git a/docs/en/concepts.md b/docs/en/concepts.md
@@ -62,7 +62,7 @@ $ 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==5.0.2 pyspark==3.3.1 jupyter
+$ pip install spark-nlp==5.1.0 pyspark==3.3.1 jupyter
 $ jupyter notebook
 ```