Add Pooling Average to Broken XXXForSentenceEmbedding annotators (#14328)

* SPARKNLP-1036: Onnx Example notebooks (#14234)

* SPARKNLP-1036: Fix dev python kernel names

* SPARKNLP-1036: Bump transformers version

* SPARKNLP-1036: Fix Colab buttons

* SPARKNLP-1036: Pin onnx version for compatibility

* SPARKNLP-1036: Upgrade Spark version

* SPARKNLP-1036: Minor Fixes

* SPARKNLP-1036: Clean Metadata

* SPARKNLP-1036: Add/Adjust Documentation

- Note for supported Spark Version of Annotators
- added missing Documentation for BGEEmbeddings

* Fixies (#14307)

* adding fix for broken annotators

---------

Co-authored-by: Devin Ha <33089471+DevinTDHa@users.noreply.github.com>
Co-authored-by: Lev <agsfer@gmail.com>
Co-authored-by: Maziyar Panahi <maziyar.panahi@iscpif.fr>

Author: 4 people

docs/en/annotator_entries/BGEEmbeddings.md

@@ -0,0 +1,160 @@
+{%- capture title -%}
+BGEEmbeddings
+{%- endcapture -%}
+
+{%- capture description -%}
+Sentence embeddings using BGE.
+
+BGE, or BAAI General Embeddings, a model that can map any text to a low-dimensional dense
+vector which can be used for tasks like retrieval, classification, clustering, or semantic
+search.
+
+Note that this annotator is only supported for Spark Versions 3.4 and up.
+
+Pretrained models can be loaded with `pretrained` of the companion object:
+
+```scala
+val embeddings = BGEEmbeddings.pretrained()
+  .setInputCols("document")
+  .setOutputCol("embeddings")
+```
+
+The default model is `"bge_base"`, if no name is provided.
+
+For available pretrained models please see the
+[Models Hub](https://sparknlp.org/models?q=BGE).
+
+For extended examples of usage, see
+[BGEEmbeddingsTestSpec](https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/BGEEmbeddingsTestSpec.scala).
+
+**Sources** :
+
+[C-Pack: Packaged Resources To Advance General Chinese Embedding](https://arxiv.org/pdf/2309.07597)
+
+[BGE Github Repository](https://github.com/FlagOpen/FlagEmbedding)
+
+**Paper abstract**
+
+*We introduce C-Pack, a package of resources that significantly advance the field of general
+Chinese embeddings. C-Pack includes three critical resources. 1) C-MTEB is a comprehensive
+benchmark for Chinese text embeddings covering 6 tasks and 35 datasets. 2) C-MTP is a massive
+text embedding dataset curated from labeled and unlabeled Chinese corpora for training
+embedding models. 3) C-TEM is a family of embedding models covering multiple sizes. Our models
+outperform all prior Chinese text embeddings on C-MTEB by up to +10% upon the time of the
+release. We also integrate and optimize the entire suite of training methods for C-TEM. Along
+with our resources on general Chinese embedding, we release our data and models for English
+text embeddings. The English models achieve stateof-the-art performance on the MTEB benchmark;
+meanwhile, our released English data is 2 times larger than the Chinese data. All these
+resources are made publicly available at https://github.com/FlagOpen/FlagEmbedding.*
+{%- 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
+documentAssembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("document")
+embeddings = BGEEmbeddings.pretrained() \
+    .setInputCols(["document"]) \
+    .setOutputCol("bge_embeddings")
+embeddingsFinisher = EmbeddingsFinisher() \
+    .setInputCols(["bge_embeddings"]) \
+    .setOutputCols("finished_embeddings") \
+    .setOutputAsVector(True)
+pipeline = Pipeline().setStages([
+    documentAssembler,
+    embeddings,
+    embeddingsFinisher
+])
+data = spark.createDataFrame([["query: how much protein should a female eat",
+"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day." + \
+"But, as you can see from this chart, you'll need to increase that if you're expecting or training for a" + \
+"marathon. Check out the chart below to see how much protein you should be eating each day.",
+]]).toDF("text")
+result = pipeline.fit(data).transform(data)
+result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
++--------------------------------------------------------------------------------+
+|                                                                          result|
++--------------------------------------------------------------------------------+
+|[[8.0190285E-4, -0.005974853, -0.072875895, 0.007944068, 0.026059335, -0.0080...|
+|[[0.050514214, 0.010061974, -0.04340176, -0.020937217, 0.05170225, 0.01157857...|
++--------------------------------------------------------------------------------+
+{%- endcapture -%}
+
+{%- capture scala_example -%}
+import spark.implicits._
+import com.johnsnowlabs.nlp.base.DocumentAssembler
+import com.johnsnowlabs.nlp.annotators.Tokenizer
+import com.johnsnowlabs.nlp.embeddings.BGEEmbeddings
+import com.johnsnowlabs.nlp.EmbeddingsFinisher
+import org.apache.spark.ml.Pipeline
+
+val documentAssembler = new DocumentAssembler()
+  .setInputCol("text")
+  .setOutputCol("document")
+
+val embeddings = BGEEmbeddings.pretrained("bge_base", "en")
+  .setInputCols("document")
+  .setOutputCol("bge_embeddings")
+
+val embeddingsFinisher = new EmbeddingsFinisher()
+  .setInputCols("bge_embeddings")
+  .setOutputCols("finished_embeddings")
+  .setOutputAsVector(true)
+
+val pipeline = new Pipeline().setStages(Array(
+  documentAssembler,
+  embeddings,
+  embeddingsFinisher
+))
+
+val data = Seq("query: how much protein should a female eat",
+"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day." +
+But, as you can see from this chart, you'll need to increase that if you're expecting or training for a" +
+marathon. Check out the chart below to see how much protein you should be eating each day."
+
+).toDF("text")
+val result = pipeline.fit(data).transform(data)
+
+result.selectExpr("explode(finished_embeddings) as result").show(1, 80)
++--------------------------------------------------------------------------------+
+|                                                                          result|
++--------------------------------------------------------------------------------+
+|[[8.0190285E-4, -0.005974853, -0.072875895, 0.007944068, 0.026059335, -0.0080...|
+|[[0.050514214, 0.010061974, -0.04340176, -0.020937217, 0.05170225, 0.01157857...|
++--------------------------------------------------------------------------------+
+{%- endcapture -%}
+
+{%- capture api_link -%}
+[BGEEmbeddings](/api/com/johnsnowlabs/nlp/embeddings/BGEEmbeddings)
+{%- endcapture -%}
+
+{%- capture python_api_link -%}
+[BGEEmbeddings](/api/python/reference/autosummary/sparknlp/annotator/embeddings/bge_embeddings/index.html#sparknlp.annotator.embeddings.bge_embeddings.BGEEmbeddings)
+{%- endcapture -%}
+
+{%- capture source_link -%}
+[BGEEmbeddings](https://github.com/JohnSnowLabs/spark-nlp/tree/master/src/main/scala/com/johnsnowlabs/nlp/embeddings/BGEEmbeddings.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="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."%}
 {% include templates/anno_table_entry.md path="" name="Chunk2Doc" summary="Converts a `CHUNK` type column back into `DOCUMENT`. Useful when trying to re-tokenize or do further analysis on a `CHUNK` result."%}
 {% include templates/anno_table_entry.md path="" name="ChunkEmbeddings" summary="This annotator utilizes WordEmbeddings, BertEmbeddings etc. to generate chunk embeddings from either Chunker, NGramGenerator, or NerConverter outputs."%}

docs/en/auxiliary.md

@@ -66,7 +66,7 @@ import com.johnsnowlabs.nlp.Annotation
 **Examples:**
 
 Complete usage examples can be seen here:
-https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/234-release-candidate/jupyter/annotation/english/spark-nlp-basics/spark-nlp-basics-functions.ipynb
+[https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/234-release-candidate/jupyter/annotation/english/spark-nlp-basics/spark-nlp-basics-functions.ipynb](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/234-release-candidate/jupyter/annotation/english/spark-nlp-basics/spark-nlp-basics-functions.ipynb)
 
 <div class="tabs-box tabs-new" markdown="1">
 

docs/en/install.md

@@ -760,7 +760,7 @@ Finally, use **jupyter_notebook_config.json** for the password:
 
 In order to fully take advantage of Spark NLP on Windows (8 or 10), you need to setup/install Apache Spark, Apache Hadoop, Java and a Pyton environment correctly by following the following instructions: [https://github.com/JohnSnowLabs/spark-nlp/discussions/1022](https://github.com/JohnSnowLabs/spark-nlp/discussions/1022)
 
-</div><div class="h3-box" markdown="1">\
+</div><div class="h3-box" markdown="1">
 
 ### How to correctly install Spark NLP on Windows
 

docs/en/transformer_entries/E5Embeddings.md

@@ -8,6 +8,8 @@ Sentence embeddings using E5.
 E5, an instruction-finetuned text embedding model that can generate text embeddings tailored
 to any task (e.g., classification, retrieval, clustering, text evaluation, etc.)
 
+Note that this annotator is only supported for Spark Versions 3.4 and up.
+
 Pretrained models can be loaded with `pretrained` of the companion object:
 
 ```scala

docs/en/transformer_entries/MPNetEmbeddings.md

@@ -10,6 +10,8 @@ Understanding by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu. MPNet a
 pre-training method, named masked and permuted language modeling, to inherit the advantages of
 masked language modeling and permuted language modeling for natural language understanding.
 
+Note that this annotator is only supported for Spark Versions 3.4 and up.
+
 Pretrained models can be loaded with `pretrained` of the companion object:
 
 ```scala

docs/en/transformers.md

@@ -9,7 +9,7 @@ modify_date: "2023-06-18"
 use_language_switcher: "Python-Scala-Java"
 show_nav: true
 sidebar:
-nav: sparknlp
+    nav: sparknlp
 ---
 
 <script> {% include scripts/transformerUseCaseSwitcher.js %} </script>

docs/index.md

@@ -314,7 +314,7 @@ data:
 
         - title:
           image:
-            src: https://upload.wikimedia.org/wikipedia/fr/thumb/8/8e/Centre_national_de_la_recherche_scientifique.svg/2048px-Centre_national_de_la_recherche_scientifique.svg.png
+            src: https://iscpif.fr/wp-content/uploads/2023/11/Logo-CNRS-ISCPIF.png
             url: https://iscpif.fr/
             style: "padding: 30px;"
             is_row: true
@@ -344,7 +344,7 @@ data:
             is_row: true
         - title:
           image:
-            src: https://upload.wikimedia.org/wikipedia/commons/thumb/f/f1/Columbia_University_shield.svg/1184px-Columbia_University_shield.svg.png
+            src: https://miro.medium.com/v2/resize:fit:1024/0*3qIWoFnZgVUtsXB-.png
             url: https://www.columbia.edu/
             style: "padding: 25px;"
             is_row: true