Spark NLP 5.5.2 Release Candidate

CHANGELOG

@@ -1,3 +1,37 @@
+========
+5.5.2
+========
+----------------
+New Features & Enhancements
+----------------
+* OpenVINO Support for Transformers (PR #14408):
+Added OpenVINO inference support to a broad range of transformer-based annotators, including DeBertaForQuestionAnswering, DeBertaForSequenceClassification, RoBertaForTokenClassification, XlmRobertaForZeroShotClassification, BartTransformer, GPT2Transformer, and many others.
+* BLIPForQuestionAnswering Transformer (PR #14422):
+Introduced a new transformer BLIPForQuestionAnswering for image-based question answering tasks. The transformer processes images alongside associated questions to provide relevant answers.
+* AutoGGUFEmbeddings Annotator (PR #14433):
+Added AutoGGUFEmbeddings to support embeddings from AutoGGUFModels, providing rich sentence embeddings. Includes an end-to-end example notebook for usage.
+* HTML Parsing into DataFrame (PR #14449):
+Introduced sparknlp.read().html() to parse local or remote HTML files and convert them into structured Spark DataFrames for easier analysis.
+* Email Parsing into DataFrame (PR #14455):
+Added sparknlp.read().email() method to parse email files into structured DataFrames, enabling scalable analysis of email content. (Note: Dependent on #14449)
+* Microsoft Word Document Parsing into DataFrame (PR #14476):
+Added a new feature to parse .docx and .doc files into a Spark DataFrame, streamlining the integration of Word documents into NLP pipelines.
+* Microsoft Fabric Support (PR #14467):
+Introduced support for leveraging Microsoft Fabric for word embeddings storage and retrieval, enhancing scalability and efficiency.
+* cuDNN Upgrade Instructions on Databricks (PR #14451):
+Added instructions on upgrading cuDNN for GPU inference and cleaned up redundant Databricks installation instructions.
+* ChunkEmbeddings Metadata Preservation (PR #14462):
+Modified ChunkEmbeddings to preserve the original chunk’s metadata in the resulting embeddings, ensuring richer contextual information is retained.
+* Default Names and Languages for Annotators (PR #14469):
+Updated default names and language configurations for newly created seq2seq annotators to improve consistency and clarity.
+
+----------------
+Bug Fixes
+----------------
+* Spark Version Errors (PR #14467):
+Resolved issues related to long Spark versions when integrating Microsoft Fabric support.
+
+
 ========
 5.5.1
 ========

README.md

@@ -55,15 +55,15 @@ documentation and examples
 
 ## Quick Start
 
-This is a quick example of how to use Spark NLP pre-trained pipeline in Python and PySpark:
+This is a quick example of how to use a Spark NLP pre-trained pipeline in Python and PySpark:
 
 ```sh
 $ java -version
 # should be Java 8 or 11 (Oracle or OpenJDK)
 $ 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==5.5.1 pyspark==3.3.1
+$ pip install spark-nlp==5.5.2 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 *5.5.1* 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 *5.5.2* 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, 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 5.5.1 has been tested and is compatible with the following runtimes:
+Spark NLP 5.5.2 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 5.5.1 has been tested and is compatible with the following EMR releases:
+Spark NLP 5.5.2 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 S5.5.1r](https://github.com/maziyarpanahi/spark-nlp-starter)
+projects [Spark NLP SBT S5.5.2r](https://github.com/maziyarpanahi/spark-nlp-starter)
 
 ### Python
 
@@ -214,7 +214,7 @@ Check all available installations for Python in our official [documentation](htt
 
 ### Compiled JARs
 
-To compile the jars from source follow [these instructions](https://sparknlp.org/docs/en/compiled#jars) from our official documenation
+To compile the jars from source follow [these instructions](https://sparknlp.org/docs/en/compiled#jars) from our official documentation
 
 ## Platform-Specific Instructions
 
@@ -234,7 +234,7 @@ For detailed instructions on how to use Spark NLP on supported platforms, please
 
 Spark NLP library and all the pre-trained models/pipelines can be used entirely offline with no access to the Internet.
 Please check [these instructions](https://sparknlp.org/docs/en/install#s3-integration) from our official documentation
-to use Spark NLP offline
+to use Spark NLP offline.
 
 ## Advanced Settings
 
@@ -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.
 
-## Document5.5.1
+## Document5.5.2
 
 ### 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.}
     }
-}5.5.1
+}5.5.2
 ```
 
 ## Community support

build.sbt

@@ -6,7 +6,7 @@ name := getPackageName(is_silicon, is_gpu, is_aarch64)
 
 organization := "com.johnsnowlabs.nlp"
 
-version := "5.5.1"
+version := "5.5.2"
 
 (ThisBuild / scalaVersion) := scalaVer
 
@@ -157,7 +157,14 @@ lazy val utilDependencies = Seq(
   greex,
   azureIdentity,
   azureStorage,
-  jsoup)
+  jsoup,
+  jakartaMail,
+  angusMail,
+  poiDocx
+    exclude ("org.apache.logging.log4j", "log4j-api"),
+  scratchpad
+    exclude ("org.apache.logging.log4j", "log4j-api")
+)
 
 lazy val typedDependencyParserDependencies = Seq(junit)
 
@@ -230,6 +237,7 @@ lazy val root = (project in file("."))
 
 (assembly / assemblyMergeStrategy) := {
   case PathList("META-INF", "versions", "9", "module-info.class") => MergeStrategy.discard
+  case PathList("module-info.class") => MergeStrategy.discard // Discard any module-info.class globally
   case PathList("apache.commons.lang3", _ @_*) => MergeStrategy.discard
   case PathList("org.apache.hadoop", _ @_*) => MergeStrategy.first
   case PathList("com.amazonaws", _ @_*) => MergeStrategy.last

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==5.5.1
+    $ pip install spark-nlp==5.5.2
 
     # 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_2.12:5.5.1")
+    .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:5.5.2")
     .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_2.12:5.5.1
+  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.5.2
 ```
 
 **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_2.12:5.5.1
+  --packages com.johnsnowlabs.nlp:spark-nlp_2.12:5.5.2
 ```
 
 **Databricks:**
@@ -96,6 +96,16 @@ spark.jsl.settings.annotator.log_folder dbfs:/PATH_TO_LOGS
 
 NOTE: If this is an existing cluster, after adding new configs or changing existing properties you need to restart it.
 
+#### Additional Configuration for Databricks
+When running Email Reader feature `sparknlp.read().email("./email-files")` on Databricks, it is necessary to include the following Spark configurations to avoid dependency conflicts:
+
+```bash
+spark.driver.userClassPathFirst true
+spark.executor.userClassPathFirst true
+```
+These configurations are required because the Databricks runtime environment includes a bundled version of the `com.sun.mail:jakarta.mail` library, which conflicts with `jakarta.activation`.
+By setting these properties, the application ensures that the user-provided libraries take precedence over those bundled in the Databricks environment, resolving the dependency conflict.
+
 </div><div class="h3-box" markdown="1">
 
 ### S3 Integration

docs/en/annotator_entries/AutoGGUFEmbeddings.md

@@ -0,0 +1,123 @@
+{%- capture title -%}
+AutoGGUFEmbeddings
+{%- endcapture -%}
+
+{%- capture description -%}
+Annotator that uses the llama.cpp library to generate text embeddings with large language
+models.
+
+The type of embedding pooling can be set with the `setPoolingType` method. The default is
+`"MEAN"`. The available options are `"NONE"`, `"MEAN"`, `"CLS"`, and `"LAST"`.
+
+If the parameters are not set, the annotator will default to use the parameters provided by
+the model.
+
+Pretrained models can be loaded with `pretrained` of the companion object:
+
+```scala
+val autoGGUFEmbeddings = AutoGGUFEmbeddings.pretrained()
+  .setInputCols("document")
+  .setOutputCol("embeddings")
+```
+
+The default model is `"nomic-embed-text-v1.5.Q8_0.gguf"`, if no name is provided.
+
+For available pretrained models please see the [Models Hub](https://sparknlp.org/models).
+
+For extended examples of usage, see the
+[AutoGGUFEmbeddingsTest](https://github.com/JohnSnowLabs/spark-nlp/tree/master/src/test/scala/com/johnsnowlabs/nlp/annotators/seq2seq/AutoGGUFEmbeddingsTest.scala)
+and the
+[example notebook](https://github.com/JohnSnowLabs/spark-nlp/tree/master/examples/python/llama.cpp/llama.cpp_in_Spark_NLP_AutoGGUFEmbeddings.ipynb).
+
+**Note**: To use GPU inference with this annotator, make sure to use the Spark NLP GPU package and set
+the number of GPU layers with the `setNGpuLayers` method.
+
+When using larger models, we recommend adjusting GPU usage with `setNCtx` and `setNGpuLayers`
+according to your hardware to avoid out-of-memory errors.
+{%- endcapture -%}
+
+{%- capture input_anno -%}
+DOCUMENT
+{%- endcapture -%}
+
+{%- capture output_anno -%}
+SENTENCE_EMBEDDINGS
+{%- endcapture -%}
+
+{%- capture python_example -%}
+>>> import sparknlp
+>>> from sparknlp.base import *
+>>> from sparknlp.annotator import *
+>>> from pyspark.ml import Pipeline
+>>> document = DocumentAssembler() \
+...     .setInputCol("text") \
+...     .setOutputCol("document")
+>>> autoGGUFEmbeddings = AutoGGUFEmbeddings.pretrained() \
+...     .setInputCols(["document"]) \
+...     .setOutputCol("completions") \
+...     .setBatchSize(4) \
+...     .setNGpuLayers(99) \
+...     .setPoolingType("MEAN")
+>>> pipeline = Pipeline().setStages([document, autoGGUFEmbeddings])
+>>> data = spark.createDataFrame([["The moons of Jupiter are 77 in total, with 79 confirmed natural satellites and 2 man-made ones."]]).toDF("text")
+>>> result = pipeline.fit(data).transform(data)
+>>> result.select("completions").show()
++--------------------------------------------------------------------------------+
+|                                                                      embeddings|
++--------------------------------------------------------------------------------+
+|[[-0.034486726, 0.07770534, -0.15982522, -0.017873349, 0.013914132, 0.0365736...|
++--------------------------------------------------------------------------------+
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import com.johnsnowlabs.nlp.base._
+import com.johnsnowlabs.nlp.annotator._
+import org.apache.spark.ml.Pipeline
+import spark.implicits._
+
+val document = new DocumentAssembler().setInputCol("text").setOutputCol("document")
+
+val autoGGUFEmbeddings = AutoGGUFEmbeddings
+  .pretrained()
+  .setInputCols("document")
+  .setOutputCol("embeddings")
+  .setBatchSize(4)
+  .setPoolingType("MEAN")
+
+val pipeline = new Pipeline().setStages(Array(document, autoGGUFEmbeddings))
+
+val data = Seq(
+  "The moons of Jupiter are 77 in total, with 79 confirmed natural satellites and 2 man-made ones.")
+  .toDF("text")
+val result = pipeline.fit(data).transform(data)
+result.select("embeddings.embeddings").show(1, truncate=80)
++--------------------------------------------------------------------------------+
+|                                                                      embeddings|
++--------------------------------------------------------------------------------+
+|[[-0.034486726, 0.07770534, -0.15982522, -0.017873349, 0.013914132, 0.0365736...|
++--------------------------------------------------------------------------------+
+{%- endcapture -%}
+
+{%- capture api_link -%}
+[AutoGGUFEmbeddings](/api/com/johnsnowlabs/nlp/embeddings/AutoGGUFEmbeddings)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[AutoGGUFEmbeddings](/api/python/reference/autosummary/sparknlp/annotator/embeddings/auto_gguf_embeddings/index.html)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[AutoGGUFEmbeddings](https://github.com/JohnSnowLabs/spark-nlp/tree/master/src/main/scala/com/johnsnowlabs/nlp/embeddings/AutoGGUFEmbeddings.scala)
+{%- endcapture -%}
+
+{% include templates/anno_template.md
+title=title
+description=description
+input_anno=input_anno
+output_anno=output_anno
+python_example=python_example
+scala_example=scala_example
+api_link=api_link
+python_api_link=python_api_link
+source_link=source_link
+%}

docs/en/annotators.md

@@ -45,6 +45,7 @@ There are two types of Annotators:
 {:.table-model-big}
 |Annotator|Description|Version |
 |---|---|---|
+{% include templates/anno_table_entry.md path="" name="AutoGGUFEmbeddings" summary="Annotator that uses the llama.cpp library to generate text embeddings with large language models."%}
 {% include templates/anno_table_entry.md path="" name="AutoGGUFModel" summary="Annotator that uses the llama.cpp library to generate text completions with large language models."%}
 {% include templates/anno_table_entry.md path="" name="BGEEmbeddings" summary="Sentence embeddings using BGE."%}
 {% include templates/anno_table_entry.md path="" name="BigTextMatcher" summary="Annotator to match exact phrases (by token) provided in a file against a Document."%}

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==5.5.1 pyspark==3.3.1 jupyter
+$ pip install spark-nlp==5.5.2 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==5.5.1 pyspark==3.3.1
+$ pip install spark-nlp==5.5.2 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 5.5.1
+!bash colab.sh -p 3.2.3 -s 5.5.2
 ```
 
 [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 5.5.1 is built with TensorFlow 2.7.1 and the following NVIDIA® software are only required for GPU support:
+Spark NLP 5.5.2 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