Bump to 5.5.1 and update CHANGELOG [run doc]

Author: maziyarpanahi

CHANGELOG

@@ -1,3 +1,36 @@
+========
+5.5.1
+========
+----------------
+New Features & Enhancements
+----------------
+* `BertForMultipleChoice` Transformer Added. Enhanced BERT’s capabilities to handle multiple-choice tasks such as standardized test questions and survey or quiz automation.
+* Integrated New Tasks and Documentation:
+  * Added support and documentation for the following tasks:
+  * Automatic Speech Recognition
+  * Dependency Parsing
+  * Image Captioning
+  * Image Classification
+  * Landing Page
+  * Question Answering
+  * Summarization
+  * Table Question Answering
+  * Text Classification
+  * Text Generation
+  * Text Preprocessing
+  * Token Classification
+  * Translation
+  * Zero-Shot Classification
+  * Zero-Shot Image Classification
+* `PromptAssembler` Annotator Introduced. Introduced a new annotator that constructs prompts for LLMs using a chat template and a sequence of messages. Accepts an array of tuples with roles (“system”, “user”, “assistant”) and message texts. Utilizes llama.cpp as a backend for template parsing, supporting basic template applications.
+
+----------------
+Bug Fixes
+----------------
+* Resolved Pretrained Model Loading Issue on DBFS Systems.
+* Fixed a bug where pretrained models were not found when running AutoGGUF model pipelines on Databricks due to incorrect path handling of gguf files.
+
+
 ========
 5.5.0
 ========

README.md

@@ -63,7 +63,7 @@ $ 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==5.5.0 pyspark==3.3.1
+$ pip install spark-nlp==5.5.1 pyspark==3.3.1
 ```
 
 In Python console or Jupyter `Python3` kernel:
@@ -129,7 +129,7 @@ For a quick example of using pipelines and models take a look at our official [d
 
 ### Apache Spark Support
 
-Spark NLP *5.5.0* 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 *5.5.1* 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 |
 |-----------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|
@@ -157,7 +157,7 @@ Find out more about 4.x `SparkNLP` versions in our official [documentation](http
 
 ### Databricks Support
 
-Spark NLP 5.5.0 has been tested and is compatible with the following runtimes:
+Spark NLP 5.5.1 has been tested and is compatible with the following runtimes:
 
 | **CPU**            | **GPU**            |
 |--------------------|--------------------|
@@ -174,7 +174,7 @@ We are compatible with older runtimes. For a full list check databricks support
 
 ### EMR Support
 
-Spark NLP 5.5.0 has been tested and is compatible with the following EMR releases:
+Spark NLP 5.5.1 has been tested and is compatible with the following EMR releases:
 
 | **EMR Release**    |
 |--------------------|
@@ -205,7 +205,7 @@ deployed to Maven central. To add any of our packages as a dependency in your ap
 from our official documentation.
 
 If you are interested, there is a simple SBT project for Spark NLP to guide you on how to use it in your
-projects [Spark NLP SBT S5.5.0r](https://github.com/maziyarpanahi/spark-nlp-starter)
+projects [Spark NLP SBT S5.5.1r](https://github.com/maziyarpanahi/spark-nlp-starter)
 
 ### Python
 
@@ -250,7 +250,7 @@ In Spark NLP we can define S3 locations to:
 
 Please check [these instructions](https://sparknlp.org/docs/en/install#s3-integration) from our official documentation.
 
-## Document5.5.0
+## Document5.5.1
 
 ### Examples
 
@@ -283,7 +283,7 @@ the Spark NLP library:
     keywords = {Spark, Natural language processing, Deep learning, Tensorflow, Cluster},
     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.}
     }
-}5.5.0
+}5.5.1
 ```
 
 ## Community support

build.sbt

@@ -6,7 +6,7 @@ name := getPackageName(is_silicon, is_gpu, is_aarch64)
 
 organization := "com.johnsnowlabs.nlp"
 
-version := "5.5.0"
+version := "5.5.1"
 
 (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==5.5.0
+    $ pip install spark-nlp==5.5.1
 
     # Using Anaconda/Conda
     $ conda install -c johnsnowlabs spark-nlp

docs/en/advanced_settings.md

@@ -52,7 +52,7 @@ 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_2.12:5.5.0")
+    .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:5.5.1")
     .getOrCreate()
 ```
 
@@ -66,7 +66,7 @@ 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_2.12:5.5.0
+  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.5.1
 ```
 
 **pyspark:**
@@ -79,7 +79,7 @@ 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_2.12:5.5.0
+  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.5.1
 ```
 
 **Databricks:**

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==5.5.0 pyspark==3.3.1 jupyter
+$ pip install spark-nlp==5.5.1 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==5.5.0 pyspark==3.3.1
+$ pip install spark-nlp==5.5.1 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 5.5.0
+!bash colab.sh -p 3.2.3 -s 5.5.1
 ```
 
 [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) 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

@@ -50,7 +50,7 @@ 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 5.5.0 is built with TensorFlow 2.7.1 and the following NVIDIA® software are only required for GPU support:
+Spark NLP 5.5.1 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