Release/531 release candidate

CHANGELOG

@@ -1,3 +1,15 @@
+========
+5.3.1
+========
+----------------
+Bug Fixes
+----------------
+* Fix M2M100 not working on the second run (closing the ONNX Session by mistake)
+* Fix ONNX models failing in clusters like Databricks
+* Fix `ZeroShotNerClassification` issue with NerConverter
+* adding colab notebook for M2M100
+
+
 ========
 5.3.0
 ========

README.md

@@ -165,7 +165,7 @@ To use Spark NLP you need the following requirements:
 
 **GPU (optional):**
 
-Spark NLP 5.3.0 is built with ONNX 1.17.0 and TensorFlow 2.7.1 deep learning engines. The minimum following NVIDIA® software are only required for GPU support:
+Spark NLP 5.3.1 is built with ONNX 1.17.0 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
@@ -181,7 +181,7 @@ $ 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==5.3.0 pyspark==3.3.1
+$ pip install spark-nlp==5.3.1 pyspark==3.3.1
 ```
 
 In Python console or Jupyter `Python3` kernel:
@@ -226,7 +226,7 @@ For more examples, you can visit our dedicated [examples](https://github.com/Joh
 
 ## Apache Spark Support
 
-Spark NLP *5.3.0* 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 *5.3.1* 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 |
 |-----------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|
@@ -270,7 +270,7 @@ Find out more about `Spark NLP` versions from our [release notes](https://github
 
 ## Databricks Support
 
-Spark NLP 5.3.0 has been tested and is compatible with the following runtimes:
+Spark NLP 5.3.1 has been tested and is compatible with the following runtimes:
 
 **CPU:**
 
@@ -343,7 +343,7 @@ Spark NLP 5.3.0 has been tested and is compatible with the following runtimes:
 
 ## EMR Support
 
-Spark NLP 5.3.0 has been tested and is compatible with the following EMR releases:
+Spark NLP 5.3.1 has been tested and is compatible with the following EMR releases:
 
 - emr-6.2.0
 - emr-6.3.0
@@ -393,11 +393,11 @@ 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_2.12:5.3.0
+spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.3.1
 
-pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.3.0
+pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.3.1
 
-spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.3.0
+spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.3.1
 ```
 
 The `spark-nlp` has been published to
@@ -406,11 +406,11 @@ the [Maven Repository](https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/s
 ```sh
 # GPU
 
-spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.3.0
+spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.3.1
 
-pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.3.0
+pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.3.1
 
-spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.3.0
+spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:5.3.1
 
 ```
 
@@ -420,11 +420,11 @@ the [Maven Repository](https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/s
 ```sh
 # AArch64
 
-spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.3.0
+spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.3.1
 
-pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.3.0
+pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.3.1
 
-spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.3.0
+spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:5.3.1
 
 ```
 
@@ -434,11 +434,11 @@ 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_2.12:5.3.0
+spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.3.1
 
-pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.3.0
+pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.3.1
 
-spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.3.0
+spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:5.3.1
 
 ```
 
@@ -452,7 +452,7 @@ set in your SparkSession:
 spark-shell \
   --driver-memory 16g \
   --conf spark.kryoserializer.buffer.max=2000M \
-  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.3.0
+  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.3.1
 ```
 
 ## Scala
@@ -470,7 +470,7 @@ coordinates:
 <dependency>
     <groupId>com.johnsnowlabs.nlp</groupId>
     <artifactId>spark-nlp_2.12</artifactId>
-    <version>5.3.0</version>
+    <version>5.3.1</version>
 </dependency>
 ```
 
@@ -481,7 +481,7 @@ coordinates:
 <dependency>
     <groupId>com.johnsnowlabs.nlp</groupId>
     <artifactId>spark-nlp-gpu_2.12</artifactId>
-    <version>5.3.0</version>
+    <version>5.3.1</version>
 </dependency>
 ```
 
@@ -492,7 +492,7 @@ coordinates:
 <dependency>
     <groupId>com.johnsnowlabs.nlp</groupId>
     <artifactId>spark-nlp-aarch64_2.12</artifactId>
-    <version>5.3.0</version>
+    <version>5.3.1</version>
 </dependency>
 ```
 
@@ -503,7 +503,7 @@ coordinates:
 <dependency>
     <groupId>com.johnsnowlabs.nlp</groupId>
     <artifactId>spark-nlp-silicon_2.12</artifactId>
-    <version>5.3.0</version>
+    <version>5.3.1</version>
 </dependency>
 ```
 
@@ -513,28 +513,28 @@ coordinates:
 
 ```sbtshell
 // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp
-libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "5.3.0"
+libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "5.3.1"
 ```
 
 **spark-nlp-gpu:**
 
 ```sbtshell
 // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-gpu
-libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu" % "5.3.0"
+libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu" % "5.3.1"
 ```
 
 **spark-nlp-aarch64:**
 
 ```sbtshell
 // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-aarch64
-libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-aarch64" % "5.3.0"
+libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-aarch64" % "5.3.1"
 ```
 
 **spark-nlp-silicon:**
 
 ```sbtshell
 // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-silicon
-libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-silicon" % "5.3.0"
+libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-silicon" % "5.3.1"
 ```
 
 Maven
@@ -556,7 +556,7 @@ If you installed pyspark through pip/conda, you can install `spark-nlp` through
 Pip:
 
 ```bash
-pip install spark-nlp==5.3.0
+pip install spark-nlp==5.3.1
 ```
 
 Conda:
@@ -585,7 +585,7 @@ 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_2.12:5.3.0")
+    .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:5.3.1")
     .getOrCreate()
 ```
 
@@ -656,7 +656,7 @@ Use either one of the following options
 - Add the following Maven Coordinates to the interpreter's library list
 
 ```bash
-com.johnsnowlabs.nlp:spark-nlp_2.12:5.3.0
+com.johnsnowlabs.nlp:spark-nlp_2.12:5.3.1
 ```
 
 - Add a path to pre-built jar from [here](#compiled-jars) in the interpreter's library list making sure the jar is
@@ -667,7 +667,7 @@ com.johnsnowlabs.nlp:spark-nlp_2.12:5.3.0
 Apart from the previous step, install the python module through pip
 
 ```bash
-pip install spark-nlp==5.3.0
+pip install spark-nlp==5.3.1
 ```
 
 Or you can install `spark-nlp` from inside Zeppelin by using Conda:
@@ -695,7 +695,7 @@ 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==5.3.0 pyspark==3.3.1 jupyter
+$ pip install spark-nlp==5.3.1 pyspark==3.3.1 jupyter
 $ jupyter notebook
 ```
 
@@ -712,7 +712,7 @@ export PYSPARK_PYTHON=python3
 export PYSPARK_DRIVER_PYTHON=jupyter
 export PYSPARK_DRIVER_PYTHON_OPTS=notebook
 
-pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.3.0
+pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.3.1
 ```
 
 Alternatively, you can mix in using `--jars` option for pyspark + `pip install spark-nlp`
@@ -739,7 +739,7 @@ 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 5.3.0
+!wget https://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.3 -s 5.3.1
 ```
 
 [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)
@@ -762,7 +762,7 @@ 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 5.3.0
+!wget https://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.3 -s 5.3.1
 ```
 
 [Spark NLP quick start on Kaggle Kernel](https://www.kaggle.com/mozzie/spark-nlp-named-entity-recognition) is a live
@@ -781,9 +781,9 @@ 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==5.3.0` -> Install
+   3.1. Install New -> PyPI -> `spark-nlp==5.3.1` -> Install
 
-   3.2. Install New -> Maven -> Coordinates -> `com.johnsnowlabs.nlp:spark-nlp_2.12:5.3.0` -> Install
+   3.2. Install New -> Maven -> Coordinates -> `com.johnsnowlabs.nlp:spark-nlp_2.12:5.3.1` -> Install
 
 4. Now you can attach your notebook to the cluster and use Spark NLP!
 
@@ -834,7 +834,7 @@ 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_2.12:5.3.0"
+      "spark.jars.packages": "com.johnsnowlabs.nlp:spark-nlp_2.12:5.3.1"
     }
 }]
 ```
@@ -843,7 +843,7 @@ A sample of AWS CLI to launch EMR cluster:
 
 ```.sh
 aws emr create-cluster \
---name "Spark NLP 5.3.0" \
+--name "Spark NLP 5.3.1" \
 --release-label emr-6.2.0 \
 --applications Name=Hadoop Name=Spark Name=Hive \
 --instance-type m4.4xlarge \
@@ -907,7 +907,7 @@ 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_2.12:5.3.0
+  --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_2.12:5.3.1
 ```
 
 2. On an existing one, you need to install spark-nlp and spark-nlp-display packages from PyPI.
@@ -950,7 +950,7 @@ 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_2.12:5.3.0")
+    .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:5.3.1")
     .getOrCreate()
 ```
 
@@ -964,7 +964,7 @@ 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_2.12:5.3.0
+  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.3.1
 ```
 
 **pyspark:**
@@ -977,7 +977,7 @@ 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_2.12:5.3.0
+  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.3.1
 ```
 
 **Databricks:**
@@ -1249,7 +1249,7 @@ 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-5.3.0.jar")
+    .config("spark.jars", "/tmp/spark-nlp-assembly-5.3.1.jar")
     .getOrCreate()
 ```
 
@@ -1258,7 +1258,7 @@ 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-5.3.0.jar`)
+  i.e., `hdfs:///tmp/spark-nlp-assembly-5.3.1.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 := "5.3.0"
+version := "5.3.1"
 
 (ThisBuild / scalaVersion) := scalaVer
 

conda/meta.yaml

@@ -1,13 +1,13 @@
 {% set name = "spark-nlp" %}
-{% set version = "5.3.0" %}
+{% set version = "5.3.1" %}
 
 package:
   name: {{ name|lower }}
   version: {{ version }}
 
 source:
   url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/spark-nlp-{{ version }}.tar.gz
-  sha256: 2fa182f1850026fa7f9d5fbb7b92939856f78ddcc2cb2d87d56af5e2e90b97f0
+  sha256: 3680f6ca4b0ba95dafafe5c74cee5751226b34d4548824da1009e276a9ff6d2d
 
 build:
   noarch: python