Release/441 release candidate (#13767)

* Bump version to 4.4.1

* [skip test] Import examples for swin and convnext (#13755)

* SPARKNLP-800 Adding threshold parameter to all XXXForSequenceClassification and XXXForZeroShotClassification (#13760)

* adding DistilBertForZeroShotClassification in python and scala (#13765)

* adding DistilBertForZeroShotClassification in python and scala

* Fix coding style and wrong class in internal

---------

Co-authored-by: Maziyar Panahi <maziyar.panahi@iscpif.fr>

* Update CHANGELOG and docs [run doc]

* Update Scala and Python APIs

* Release 4.4.1 on Conda [skip test]

---------

Co-authored-by: Devin Ha <33089471+DevinTDHa@users.noreply.github.com>
Co-authored-by: Danilo Burbano <37355249+danilojsl@users.noreply.github.com>
Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>
Co-authored-by: github-actions <action@github.com>

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -75,7 +75,7 @@ body:
     attributes:
       label: Type of Spark Application
       multiple: true
-      options: ["spark-shell", "spark-submit", "Scala Application", "Python Appliation", "Java Application"]
+      options: ["spark-shell", "spark-submit", "Scala Application", "Python Application", "Java Application"]
   - type: input
     attributes:
       label: Java Version
@@ -87,7 +87,7 @@ body:
   - type: input
     attributes:
       label: Setup and installation
-      description: How you set up Spark NLP, e.g. Pypi, Conda, Maven, sbt, etc.
+      description: How you set up Spark NLP, e.g. PyPi, Conda, Maven, sbt, etc.
   - type: input
     attributes:
       label: Operating System and Version

CHANGELOG

@@ -1,3 +1,15 @@
+========
+4.4.1
+========
+----------------
+New Features & Enhancements
+----------------
+
+* Implement a new Zero-Shot Text Classification for DistilBERT annotator called `DistilBertForZeroShotClassification`
+* Adding `threshold` param to `AlbertForSequenceClassification`, `BertForSequenceClassification`, `BertForZeroShotClassification`, `DistilBertForSequenceClassification`, `CamemBertForSequenceClassification`, `DeBertaForSequenceClassification`, LongformerForSequenceClassification`, RoBertaForQuestionAnswering`, `XlmRoBertaForSequenceClassification`, and `XlnetForSequenceClassification` annotators
+* Add new notebooks to import models for `SwinForImageClassification` and `ConvNextForImageClassification` annotators for Image Classification
+
+
 ========
 4.4.0
 ========

README.md

@@ -165,7 +165,7 @@ To use Spark NLP you need the following requirements:
 
 **GPU (optional):**
 
-Spark NLP 4.4.0 is built with TensorFlow 2.7.1 and the following NVIDIA® software are only required for GPU support:
+Spark NLP 4.4.1 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
@@ -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==4.4.0 pyspark==3.3.1
+$ pip install spark-nlp==4.4.1 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 *4.4.0* has been built on top of Apache Spark 3.2 while fully supports Apache Spark 3.0.x, 3.1.x, 3.2.x, and
+Spark NLP *4.4.1* has been built on top of Apache Spark 3.2 while fully supports Apache Spark 3.0.x, 3.1.x, 3.2.x, and
 3.3.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 |
@@ -266,7 +266,7 @@ Find out more about `Spark NLP` versions from our [release notes](https://github
 
 ## Databricks Support
 
-Spark NLP 4.4.0 has been tested and is compatible with the following runtimes:
+Spark NLP 4.4.1 has been tested and is compatible with the following runtimes:
 
 **CPU:**
 
@@ -320,7 +320,7 @@ runtimes supporting CUDA 11 are 9.x and above as listed under GPU.
 
 ## EMR Support
 
-Spark NLP 4.4.0 has been tested and is compatible with the following EMR releases:
+Spark NLP 4.4.1 has been tested and is compatible with the following EMR releases:
 
 - emr-6.2.0
 - emr-6.3.0
@@ -364,11 +364,11 @@ 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_2.12:4.4.0
+spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.1
 
-pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0
+pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.1
 
-spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0
+spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.1
 ```
 
 The `spark-nlp` has been published to
@@ -377,11 +377,11 @@ the [Maven Repository](https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/s
 ```sh
 # GPU
 
-spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.4.0
+spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.4.1
 
-pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.4.0
+pyspark --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.4.1
 
-spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.4.0
+spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-gpu_2.12:4.4.1
 
 ```
 
@@ -391,11 +391,11 @@ the [Maven Repository](https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/s
 ```sh
 # AArch64
 
-spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.4.0
+spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.4.1
 
-pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.4.0
+pyspark --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.4.1
 
-spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.4.0
+spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-aarch64_2.12:4.4.1
 
 ```
 
@@ -405,11 +405,11 @@ 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_2.12:4.4.0
+spark-shell --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.4.1
 
-pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.4.0
+pyspark --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.4.1
 
-spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.4.0
+spark-submit --packages com.johnsnowlabs.nlp:spark-nlp-silicon_2.12:4.4.1
 
 ```
 
@@ -423,7 +423,7 @@ set in your SparkSession:
 spark-shell \
   --driver-memory 16g \
   --conf spark.kryoserializer.buffer.max=2000M \
-  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0
+  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.1
 ```
 
 ## Scala
@@ -441,7 +441,7 @@ coordinates:
 <dependency>
     <groupId>com.johnsnowlabs.nlp</groupId>
     <artifactId>spark-nlp_2.12</artifactId>
-    <version>4.4.0</version>
+    <version>4.4.1</version>
 </dependency>
 ```
 
@@ -452,7 +452,7 @@ coordinates:
 <dependency>
     <groupId>com.johnsnowlabs.nlp</groupId>
     <artifactId>spark-nlp-gpu_2.12</artifactId>
-    <version>4.4.0</version>
+    <version>4.4.1</version>
 </dependency>
 ```
 
@@ -463,7 +463,7 @@ coordinates:
 <dependency>
     <groupId>com.johnsnowlabs.nlp</groupId>
     <artifactId>spark-nlp-aarch64_2.12</artifactId>
-    <version>4.4.0</version>
+    <version>4.4.1</version>
 </dependency>
 ```
 
@@ -474,7 +474,7 @@ coordinates:
 <dependency>
     <groupId>com.johnsnowlabs.nlp</groupId>
     <artifactId>spark-nlp-silicon_2.12</artifactId>
-    <version>4.4.0</version>
+    <version>4.4.1</version>
 </dependency>
 ```
 
@@ -484,28 +484,28 @@ coordinates:
 
 ```sbtshell
 // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp
-libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "4.4.0"
+libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "4.4.1"
 ```
 
 **spark-nlp-gpu:**
 
 ```sbtshell
 // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-gpu
-libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu" % "4.4.0"
+libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu" % "4.4.1"
 ```
 
 **spark-nlp-aarch64:**
 
 ```sbtshell
 // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-aarch64
-libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-aarch64" % "4.4.0"
+libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-aarch64" % "4.4.1"
 ```
 
 **spark-nlp-silicon:**
 
 ```sbtshell
 // https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-silicon
-libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-silicon" % "4.4.0"
+libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-silicon" % "4.4.1"
 ```
 
 Maven
@@ -527,7 +527,7 @@ If you installed pyspark through pip/conda, you can install `spark-nlp` through
 Pip:
 
 ```bash
-pip install spark-nlp==4.4.0
+pip install spark-nlp==4.4.1
 ```
 
 Conda:
@@ -556,7 +556,7 @@ 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_2.12:4.4.0")
+    .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.1")
     .getOrCreate()
 ```
 
@@ -627,7 +627,7 @@ Use either one of the following options
 - Add the following Maven Coordinates to the interpreter's library list
 
 ```bash
-com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0
+com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.1
 ```
 
 - Add a path to pre-built jar from [here](#compiled-jars) in the interpreter's library list making sure the jar is
@@ -638,7 +638,7 @@ com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0
 Apart from the previous step, install the python module through pip
 
 ```bash
-pip install spark-nlp==4.4.0
+pip install spark-nlp==4.4.1
 ```
 
 Or you can install `spark-nlp` from inside Zeppelin by using Conda:
@@ -666,7 +666,7 @@ 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==4.4.0 pyspark==3.3.1 jupyter
+$ pip install spark-nlp==4.4.1 pyspark==3.3.1 jupyter
 $ jupyter notebook
 ```
 
@@ -683,7 +683,7 @@ export PYSPARK_PYTHON=python3
 export PYSPARK_DRIVER_PYTHON=jupyter
 export PYSPARK_DRIVER_PYTHON_OPTS=notebook
 
-pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0
+pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.1
 ```
 
 Alternatively, you can mix in using `--jars` option for pyspark + `pip install spark-nlp`
@@ -710,7 +710,7 @@ 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 4.4.0
+!wget https://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.3 -s 4.4.1
 ```
 
 [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)
@@ -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 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 4.4.0
+!wget https://setup.johnsnowlabs.com/colab.sh -O - | bash /dev/stdin -p 3.2.3 -s 4.4.1
 ```
 
 [Spark NLP quick start on Kaggle Kernel](https://www.kaggle.com/mozzie/spark-nlp-named-entity-recognition) is a live
@@ -752,9 +752,9 @@ 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==4.4.0` -> Install
+   3.1. Install New -> PyPI -> `spark-nlp==4.4.1` -> Install
 
-   3.2. Install New -> Maven -> Coordinates -> `com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0` -> Install
+   3.2. Install New -> Maven -> Coordinates -> `com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.1` -> Install
 
 4. Now you can attach your notebook to the cluster and use Spark NLP!
 
@@ -805,7 +805,7 @@ 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_2.12:4.4.0"
+      "spark.jars.packages": "com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.1"
     }
 }]
 ```
@@ -814,7 +814,7 @@ A sample of AWS CLI to launch EMR cluster:
 
 ```.sh
 aws emr create-cluster \
---name "Spark NLP 4.4.0" \
+--name "Spark NLP 4.4.1" \
 --release-label emr-6.2.0 \
 --applications Name=Hadoop Name=Spark Name=Hive \
 --instance-type m4.4xlarge \
@@ -878,7 +878,7 @@ 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_2.12:4.4.0
+  --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_2.12:4.4.1
 ```
 
 2. On an existing one, you need to install spark-nlp and spark-nlp-display packages from PyPI.
@@ -917,7 +917,7 @@ 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_2.12:4.4.0")
+    .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.1")
     .getOrCreate()
 ```
 
@@ -931,7 +931,7 @@ 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_2.12:4.4.0
+  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.1
 ```
 
 **pyspark:**
@@ -944,7 +944,7 @@ 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_2.12:4.4.0
+  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.1
 ```
 
 **Databricks:**
@@ -1216,7 +1216,7 @@ 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-4.4.0.jar")
+    .config("spark.jars", "/tmp/spark-nlp-assembly-4.4.1.jar")
     .getOrCreate()
 ```
 
@@ -1225,7 +1225,7 @@ spark = SparkSession.builder
   version (3.0.x, 3.1.x, 3.2.x, and 3.3.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-4.4.0.jar`)
+  i.e., `hdfs:///tmp/spark-nlp-assembly-4.4.1.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 := "4.4.0"
+version := "4.4.1"
 
 (ThisBuild / scalaVersion) := scalaVer