diff --git a/docs/en/transformers.md b/docs/en/transformers.md
index aabf942f35ef53..f73f644d3f3606 100644
--- a/docs/en/transformers.md
+++ b/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>
@@ -17,10 +17,10 @@ sidebar:
 {% assign parent_path = "en/transformer_entries" %}
 
 {% for file in site.static_files %}
-    {% if file.path contains parent_path %}
-        {% assign file_name = file.path | remove:  parent_path | remove:  "/" | prepend: "transformer_entries/" %}
-        {% include_relative {{ file_name }} %}
-    {% endif %}
+{% if file.path contains parent_path %}
+{% assign file_name = file.path | remove:  parent_path | remove:  "/" | prepend: "transformer_entries/" %}
+{% include_relative {{ file_name }} %}
+{% endif %}
 {% endfor %}
 
 <div class="h3-box" markdown="1">
@@ -29,7 +29,7 @@ sidebar:
 
 ### Overview
 
-We have extended support for `HuggingFace` 🤗   and `TF Hub` exported models since `3.1.0` to equivalent Spark NLP 🚀 annotators. Starting this release, you can easily use the `saved_model` feature in HuggingFace within a few lines of codes and import any `BERT`, `DistilBERT`, `CamemBERT`, `RoBERTa`, `DeBERTa`, `XLM-RoBERTa`, `Longformer`, `BertForTokenClassification`, `DistilBertForTokenClassification`, `AlbertForTokenClassification`, `RoBertaForTokenClassification`, `DeBertaForTokenClassification`, `XlmRoBertaForTokenClassification`, `XlnetForTokenClassification`,  `LongformerForTokenClassification`, `CamemBertForTokenClassification`, `CamemBertForSequenceClassification`, `CamemBertForQuestionAnswering`, `BertForSequenceClassification`, `DistilBertForSequenceClassification`, `AlbertForSequenceClassification`, `RoBertaForSequenceClassification`, `DeBertaForSequenceClassification`, `XlmRoBertaForSequenceClassification`, `XlnetForSequenceClassification`,  `LongformerForSequenceClassification`, `AlbertForQuestionAnswering`, `BertForQuestionAnswering`,  `DeBertaForQuestionAnswering`, `DistilBertForQuestionAnswering`, `LongformerForQuestionAnswering`, `RoBertaForQuestionAnswering`, `XlmRoBertaForQuestionAnswering`, `TapasForQuestionAnswering`, `Vision Transformers (ViT)`, `HubertForCTC`, `SwinForImageClassification`, and `ConvNextForImageClassification` models to Spark NLP. We will work on the remaining annotators and extend this support to the rest with each release 😊
+We have extended support for `HuggingFace` 🤗 and `TF Hub` exported models since `3.1.0` to equivalent Spark NLP 🚀 annotators. Starting this release, you can easily use the `saved_model` feature in HuggingFace within a few lines of codes and import any `BERT`, `DistilBERT`, `CamemBERT`, `RoBERTa`, `DeBERTa`, `XLM-RoBERTa`, `Longformer`, `BertForTokenClassification`, `DistilBertForTokenClassification`, `AlbertForTokenClassification`, `RoBertaForTokenClassification`, `DeBertaForTokenClassification`, `XlmRoBertaForTokenClassification`, `XlnetForTokenClassification`, `LongformerForTokenClassification`, `CamemBertForTokenClassification`, `CamemBertForSequenceClassification`, `CamemBertForQuestionAnswering`, `BertForSequenceClassification`, `DistilBertForSequenceClassification`, `AlbertForSequenceClassification`, `RoBertaForSequenceClassification`, `DeBertaForSequenceClassification`, `XlmRoBertaForSequenceClassification`, `XlnetForSequenceClassification`, `LongformerForSequenceClassification`, `AlbertForQuestionAnswering`, `BertForQuestionAnswering`, `DeBertaForQuestionAnswering`, `DistilBertForQuestionAnswering`, `LongformerForQuestionAnswering`, `RoBertaForQuestionAnswering`, `XlmRoBertaForQuestionAnswering`, `TapasForQuestionAnswering`, `Vision Transformers (ViT)`, `HubertForCTC`, `SwinForImageClassification`, and `ConvNextForImageClassification` models to Spark NLP. We will work on the remaining annotators and extend this support to the rest with each release 😊
 
 </div><div class="h3-box" markdown="1">
 
@@ -161,7 +161,7 @@ We have extended support for `HuggingFace` 🤗   and `TF Hub` exported models s
 | BertForTokenClassification          | [HuggingFace in Spark NLP - BertForTokenClassification](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20BertForTokenClassification.ipynb)                   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20BertForTokenClassification.ipynb)          |
 | BertForZeroShotClassification       | [HuggingFace in Spark NLP - BertForZeroShotClassification](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20BertForSequenceClassification.ipynb)             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20BertForZeroShotClassification.ipynb)       |
 | BertSentenceEmbeddings              | [HuggingFace in Spark NLP - BERT Sentence](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20BERT%20Sentence.ipynb)                                           | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20BERT%20Sentence.ipynb)                     |
-| BERTSentenceEmbeddings              | [HuggingFace in Spark NLP - BERT Sentence](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20BERT%20Sentence.ipynb)                                           | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20BERT%20Sentence.ipynb)                     |
+| BertSentenceEmbeddings - Fine Tuned | [Fine Tuned Sentence Bert in Spark NLP](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/Fine_Tuned_Sentence_Bert_in_Spark_NLP.ipynb)                                                             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/Fine_Tuned_Sentence_Bert_in_Spark_NLP.ipynb)                                    |
 | CamemBertEmbeddings                 | [HuggingFace in Spark NLP - CamemBERT](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20CamemBERT.ipynb)                                                     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20CamemBERT.ipynb)                           |
 | CamemBertForQuestionAnswering       | [HuggingFace in Spark NLP - CamemBertForQuestionAnswering](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20CamemBertForSequenceClassification.ipynb)        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20CamemBertForQuestionAnswering.ipynb)       |
 | CamemBertForSequenceClassification  | [HuggingFace in Spark NLP - CamemBertForSequenceClassification](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20CamemBertForSequenceClassification.ipynb)   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20CamemBertForSequenceClassification.ipynb)  |
@@ -199,9 +199,9 @@ We have extended support for `HuggingFace` 🤗   and `TF Hub` exported models s
 #### TF Hub to Spark NLP
 
 | Spark NLP              | TF Hub Notebooks                                                                                                                                                                  | Colab                                                                                                                                                                                                                                          |
-|:-----------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| :--------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | AlbertEmbeddings       | [TF Hub in Spark NLP - ALBERT](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/TF%20Hub%20in%20Spark%20NLP%20-%20ALBERT.ipynb)                 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/TF%20Hub%20in%20Spark%20NLP%20-%20ALBERT.ipynb)          |
 | BertEmbeddings         | [TF Hub in Spark NLP - BERT](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/TF%20Hub%20in%20Spark%20NLP%20-%20BERT.ipynb)                     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/TF%20Hub%20in%20Spark%20NLP%20-%20BERT.ipynb)            |
 | BertSentenceEmbeddings | [TF Hub in Spark NLP - BERT Sentence](https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/TF%20Hub%20in%20Spark%20NLP%20-%20BERT%20Sentence.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/TF%20Hub%20in%20Spark%20NLP%20-%20BERT%20Sentence.ipynb) |
 
-</div>
\ No newline at end of file
+</div>
diff --git a/examples/python/transformers/Fine_Tuned_Sentence_Bert_in_Spark_NLP.ipynb b/examples/python/transformers/Fine_Tuned_Sentence_Bert_in_Spark_NLP.ipynb
new file mode 100644
index 00000000000000..047d5b4869033b
--- /dev/null
+++ b/examples/python/transformers/Fine_Tuned_Sentence_Bert_in_Spark_NLP.ipynb
@@ -0,0 +1,7121 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![JohnSnowLabs](https://sparknlp.org/assets/images/logo.png)\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/Fine_Tuned_Sentence_Bert_in_Spark_NLP.ipynb)\n",
+    "\n",
+    "# Exporting Fine Tuned Sentence-BERT Models and Importing them into Spark NLP 🚀\n",
+    "\n",
+    "This notebook will go through the steps of exporting a fine tuned bert model to generate sentence embeddings. First, let's install the dependencies we need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Installing PySpark 3.2.3 and Spark NLP 5.2.3\n",
+      "setup Colab for PySpark 3.2.3 and Spark NLP 5.2.3\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m281.5/281.5 MB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.6/547.6 kB\u001b[0m \u001b[31m28.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.7/199.7 kB\u001b[0m \u001b[31m16.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25h  Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m588.3/588.3 MB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m51.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m69.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m48.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.0/6.0 MB\u001b[0m \u001b[31m48.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m439.2/439.2 kB\u001b[0m \u001b[31m36.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.9/4.9 MB\u001b[0m \u001b[31m57.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m781.3/781.3 kB\u001b[0m \u001b[31m38.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+      "pandas-gbq 0.19.2 requires google-auth-oauthlib>=0.7.0, but you have google-auth-oauthlib 0.4.6 which is incompatible.\n",
+      "tensorflow-datasets 4.9.4 requires protobuf>=3.20, but you have protobuf 3.19.6 which is incompatible.\n",
+      "tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 3.19.6 which is incompatible.\u001b[0m\u001b[31m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m507.1/507.1 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m15.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25h"
+     ]
+    }
+   ],
+   "source": [
+    "!wget -q http://setup.johnsnowlabs.com/colab.sh -O - | bash\n",
+    "\n",
+    "!pip install -q transformers==4.30.0 tensorflow==2.11.0\n",
+    "!pip install -q datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import AutoTokenizer, TFAutoModelForMaskedLM\n",
+    "from transformers import TFBertModel, BertTokenizer, TFBertForMaskedLM\n",
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "OUTPUT_PATH = \"/content/sbert_tf\"\n",
+    "! mkdir -p $OUTPUT_PATH"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exporting original models\n",
+    "\n",
+    "We first export the original model and import it into Spark NLP. We will use it later, to compare it to the fine-tuned one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
+      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+      "You will be able to reuse this secret in all of your notebooks.\n",
+      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b12a60f7e4b74b5ba4a4d6592f33a10e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4270241988d4c298b977f6164e1824a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e285ca87e974bfc8276a8c58ab00697",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a05e2d7f7e149caae6469514fbaf7ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "pytorch_model.bin:   0%|          | 0.00/436M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFBertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight']\n",
+      "- This IS expected if you are initializing TFBertModel from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing TFBertModel from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
+      "All the weights of TFBertModel were initialized from the PyTorch model.\n",
+      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n"
+     ]
+    }
+   ],
+   "source": [
+    "MODEL_NAME = \"bert-base-cased\"\n",
+    "# save tokenizer\n",
+    "tokenizer = BertTokenizer.from_pretrained(MODEL_NAME).save_pretrained(\n",
+    "    \"{}/{}_tokenizer\".format(OUTPUT_PATH, MODEL_NAME)\n",
+    ")\n",
+    "# load tokenizer\n",
+    "tokenizer = BertTokenizer.from_pretrained(MODEL_NAME)\n",
+    "\n",
+    "model = TFBertModel.from_pretrained(MODEL_NAME, from_pt=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mean_pooling(token_embeddings, attention_mask):\n",
+    "    input_mask_expanded = tf.cast(\n",
+    "        tf.repeat(\n",
+    "            tf.expand_dims(attention_mask, -1),\n",
+    "            repeats=token_embeddings.shape[-1],\n",
+    "            axis=-1,\n",
+    "        ),\n",
+    "        tf.float32,\n",
+    "    )\n",
+    "    return tf.reduce_sum(\n",
+    "        token_embeddings * input_mask_expanded, axis=1\n",
+    "    ) / tf.clip_by_value(\n",
+    "        tf.reduce_sum(input_mask_expanded, axis=1),\n",
+    "        clip_value_min=1e-9,\n",
+    "        clip_value_max=4096,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# Define TF Signature\n",
+    "@tf.function(\n",
+    "    input_signature=[\n",
+    "        {\n",
+    "            \"input_ids\": tf.TensorSpec((None, None), tf.int32, name=\"input_ids\"),\n",
+    "            \"attention_mask\": tf.TensorSpec(\n",
+    "                (None, None), tf.int32, name=\"attention_mask\"\n",
+    "            ),\n",
+    "            \"token_type_ids\": tf.TensorSpec(\n",
+    "                (None, None), tf.int32, name=\"token_type_ids\"\n",
+    "            ),\n",
+    "        }\n",
+    "    ]\n",
+    ")\n",
+    "def serving_fn(input):\n",
+    "    outputs = model(input, output_hidden_states=True)\n",
+    "    # compute sentence embedding by averaging token embeddings\n",
+    "    pooler_output = mean_pooling(outputs.hidden_states[-1], input[\"attention_mask\"])\n",
+    "    # compute sentence embedding by taking the built in pooler output,\n",
+    "    # which currently is actually the CLS embedding. This doesn't work well,\n",
+    "    # so avoid using it\n",
+    "    # pooled_output = outputs.pooler_output\n",
+    "    return {\"pooler_output\": pooler_output}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:Found untraced functions such as embeddings_layer_call_fn, embeddings_layer_call_and_return_conditional_losses, encoder_layer_call_fn, encoder_layer_call_and_return_conditional_losses, pooler_layer_call_fn while saving (showing 5 of 420). These functions will not be directly callable after loading.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# save model to local directory\n",
+    "\n",
+    "MODEL_NAME_w_sign = \"./{}_w_sign\".format(MODEL_NAME)\n",
+    "\n",
+    "model.save_pretrained(\n",
+    "    \"{}/{}\".format(OUTPUT_PATH, MODEL_NAME_w_sign),\n",
+    "    saved_model=True,\n",
+    "    signatures={\"serving_default\": serving_fn},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cp {OUTPUT_PATH}/{MODEL_NAME}_tokenizer/vocab.txt {OUTPUT_PATH}/{MODEL_NAME_w_sign}/saved_model/1/assets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <div>\n",
+       "                <p><b>SparkSession - in-memory</b></p>\n",
+       "                \n",
+       "        <div>\n",
+       "            <p><b>SparkContext</b></p>\n",
+       "\n",
+       "            <p><a href=\"http://d784806eb417:4040\">Spark UI</a></p>\n",
+       "\n",
+       "            <dl>\n",
+       "              <dt>Version</dt>\n",
+       "                <dd><code>v3.2.3</code></dd>\n",
+       "              <dt>Master</dt>\n",
+       "                <dd><code>local[*]</code></dd>\n",
+       "              <dt>AppName</dt>\n",
+       "                <dd><code>Spark NLP</code></dd>\n",
+       "            </dl>\n",
+       "        </div>\n",
+       "        \n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<pyspark.sql.session.SparkSession at 0x7d80a6220f70>"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sparknlp\n",
+    "\n",
+    "from sparknlp.annotator import *\n",
+    "from pyspark.ml import Pipeline\n",
+    "from pyspark.sql import functions as F\n",
+    "\n",
+    "spark = sparknlp.start()\n",
+    "\n",
+    "spark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sent_bert = (\n",
+    "    BertSentenceEmbeddings.loadSavedModel(\n",
+    "        \"{}/{}/saved_model/1\".format(OUTPUT_PATH, MODEL_NAME_w_sign), spark\n",
+    "    )\n",
+    "    .setInputCols(\"sentence\")\n",
+    "    .setOutputCol(\"bert_sentence\")\n",
+    "    .setCaseSensitive(True)\n",
+    "    .setDimension(768)\n",
+    "    .setStorageRef(\"sent_bert_base_cased\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sent_bert.write().overwrite().save(\"./{}_spark_nlp\".format(MODEL_NAME))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "document_assembler = DocumentAssembler().setInputCol(\"text\").setOutputCol(\"document\")\n",
+    "\n",
+    "sentence_detector = (\n",
+    "    SentenceDetector().setInputCols([\"document\"]).setOutputCol(\"sentence\")\n",
+    ")\n",
+    "\n",
+    "embeddings = (\n",
+    "    BertSentenceEmbeddings.load(\"./{}_spark_nlp\".format(MODEL_NAME))\n",
+    "    .setInputCols(\"sentence\")\n",
+    "    .setOutputCol(\"sentence_embeddings\")\n",
+    ")\n",
+    "\n",
+    "nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, embeddings])\n",
+    "\n",
+    "text = [[\"I hate cancer\"], [\"Antibiotics aren't painkiller\"]]\n",
+    "\n",
+    "data = spark.createDataFrame(text).toDF(\"text\")\n",
+    "\n",
+    "result = nlp_pipeline.fit(data).transform(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----------------------------+----------------------------------------------------------------------------------------------------+\n",
+      "|                     sentence|                                                                                          embeddings|\n",
+      "+-----------------------------+----------------------------------------------------------------------------------------------------+\n",
+      "|                I hate cancer|[0.675583, 0.05248031, -0.2677794, -0.02619921, -0.068684764, -0.038617752, 0.29574826, 0.0209077...|\n",
+      "|Antibiotics aren't painkiller|[0.3458845, -0.06992405, 0.15711522, 0.36460966, -0.04376867, -0.21441574, -0.3123266, 0.00353415...|\n",
+      "+-----------------------------+----------------------------------------------------------------------------------------------------+\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "result.select(\n",
+    "    F.explode(\n",
+    "        F.arrays_zip(result.sentence.result, result.sentence_embeddings.embeddings)\n",
+    "    ).alias(\"cols\")\n",
+    ").select(\n",
+    "    F.expr(\"cols['0']\").alias(\"sentence\"), F.expr(\"cols['1']\").alias(\"embeddings\")\n",
+    ").show(\n",
+    "    truncate=100\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's restart the session at this point, so we have some more RAM available."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training and Expoting custom fine-tuned models\n",
+    "\n",
+    "In this section, we will fine-tune a `bert-base-cased` on the `wikitext` data set. Additionally, to create sentence embeddings, we will need to create a pooling operation for the token embeddings.\n",
+    "\n",
+    "First, we load the pretrained model and the data set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
+      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+      "You will be able to reuse this secret in all of your notebooks.\n",
+      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+      "  warnings.warn(\n",
+      "All PyTorch model weights were used when initializing TFBertForMaskedLM.\n",
+      "\n",
+      "All the weights of TFBertForMaskedLM were initialized from the PyTorch model.\n",
+      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertForMaskedLM for predictions without further training.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import AutoTokenizer, BertTokenizer, TFAutoModelForMaskedLM\n",
+    "\n",
+    "OUTPUT_PATH = \"/content/sbert_tf\"\n",
+    "\n",
+    "MODEL_NAME = \"bert-base-cased\"\n",
+    "# save tokenizer\n",
+    "tokenizer = BertTokenizer.from_pretrained(MODEL_NAME).save_pretrained(\n",
+    "    \"{}/{}_tokenizer\".format(OUTPUT_PATH, MODEL_NAME)\n",
+    ")\n",
+    "# load tokenizer\n",
+    "tokenizer = BertTokenizer.from_pretrained(MODEL_NAME)\n",
+    "\n",
+    "model = TFAutoModelForMaskedLM.from_pretrained(MODEL_NAME, from_pt=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Set Pre-processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1dee46f37c0e405abe70f3857ef0d92f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading readme:   0%|          | 0.00/10.5k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2722931bd65746faba4130da430cd73e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/733k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26d75d06bb8249468e34fa99746c1a09",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/6.36M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "140a410b38964e498d1961a2ba0c37ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/657k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5c2c7a21b8c4952b3f70a9259730623",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating test split:   0%|          | 0/4358 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9216ff5977074f5ca73242031b6a70dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split:   0%|          | 0/36718 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e324ea037fba438c989e82f945e045b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating validation split:   0%|          | 0/3760 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "\n",
+    "dataset = load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need to tokenize the data to create token ids and preprocess the text into batches, so that the model can accept it as input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "decbd0e2e47447fa95b7ff4a0aeaa0f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map (num_proc=4):   0%|          | 0/4358 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Token indices sequence length is longer than the specified maximum sequence length for this model (546 > 512). Running this sequence through the model will result in indexing errors\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f8f0b308a8b43149580742caf225953",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map (num_proc=4):   0%|          | 0/36718 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Token indices sequence length is longer than the specified maximum sequence length for this model (574 > 512). Running this sequence through the model will result in indexing errors\n",
+      "Token indices sequence length is longer than the specified maximum sequence length for this model (529 > 512). Running this sequence through the model will result in indexing errors\n",
+      "Token indices sequence length is longer than the specified maximum sequence length for this model (686 > 512). Running this sequence through the model will result in indexing errors\n",
+      "Token indices sequence length is longer than the specified maximum sequence length for this model (528 > 512). Running this sequence through the model will result in indexing errors\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ffcc7c42d334a0e8dd2373f28a05871",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map (num_proc=4):   0%|          | 0/3760 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def tokenize_function(examples):\n",
+    "    return tokenizer(examples[\"text\"])\n",
+    "\n",
+    "\n",
+    "tokenized_datasets = dataset.map(\n",
+    "    tokenize_function, batched=True, num_proc=4, remove_columns=[\"text\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a7d63e31eb14bafbc059e8d6676cd28",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map (num_proc=4):   0%|          | 0/4358 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80a7c2a1e6684945b2340845dc0df3d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map (num_proc=4):   0%|          | 0/36718 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1c62df2358e443eb68f11e897289350",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map (num_proc=4):   0%|          | 0/3760 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# block_size = tokenizer.model_max_length\n",
+    "block_size = 128\n",
+    "\n",
+    "\n",
+    "def group_texts(examples):\n",
+    "    # Concatenate all texts.\n",
+    "    concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\n",
+    "    total_length = len(concatenated_examples[list(examples.keys())[0]])\n",
+    "    # We drop the small remainder, though you could add padding instead if the model supports it\n",
+    "    # In this, as in all things, we advise you to follow your heart\n",
+    "    total_length = (total_length // block_size) * block_size\n",
+    "    # Split by chunks of max_len.\n",
+    "    result = {\n",
+    "        k: [t[i : i + block_size] for i in range(0, total_length, block_size)]\n",
+    "        for k, t in concatenated_examples.items()\n",
+    "    }\n",
+    "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
+    "    return result\n",
+    "\n",
+    "\n",
+    "lm_datasets = tokenized_datasets.map(\n",
+    "    group_texts,\n",
+    "    batched=True,\n",
+    "    batch_size=1000,\n",
+    "    num_proc=4,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fine Tuning\n",
+    "\n",
+    "Now we can start the training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import create_optimizer, AdamWeightDecay\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)\n",
+    "\n",
+    "model.compile(optimizer=optimizer, jit_compile=True, metrics=[\"accuracy\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import DataCollatorForLanguageModeling\n",
+    "\n",
+    "data_collator = DataCollatorForLanguageModeling(\n",
+    "    tokenizer=tokenizer, mlm_probability=0.15, return_tensors=\"np\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_set = model.prepare_tf_dataset(\n",
+    "    lm_datasets[\"train\"],\n",
+    "    shuffle=True,\n",
+    "    batch_size=16,\n",
+    "    collate_fn=data_collator,\n",
+    ")\n",
+    "\n",
+    "validation_set = model.prepare_tf_dataset(\n",
+    "    lm_datasets[\"validation\"],\n",
+    "    shuffle=False,\n",
+    "    batch_size=16,\n",
+    "    collate_fn=data_collator,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As an example, we train the model with 1 epoch and 10 steps per epoch only. For a serious fine tune session, you might want to choose higher values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:AutoGraph could not transform <function infer_framework at 0x7e9f88722b90> and will run it as-is.\n",
+      "Cause: for/else statement not yet supported\n",
+      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: AutoGraph could not transform <function infer_framework at 0x7e9f88722b90> and will run it as-is.\n",
+      "Cause: for/else statement not yet supported\n",
+      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
+      "10/10 [==============================] - 427s 32s/step - loss: 2.5135 - accuracy: 0.0826\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x7e9e93727a90>"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(\n",
+    "    train_set,\n",
+    "    epochs=1,\n",
+    "    steps_per_epoch=10,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:Found untraced functions such as embeddings_layer_call_fn, embeddings_layer_call_and_return_conditional_losses, encoder_layer_call_fn, encoder_layer_call_and_return_conditional_losses, predictions_layer_call_fn while saving (showing 5 of 424). These functions will not be directly callable after loading.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# to save in case there is a need for hf checkponts in the future\n",
+    "FINETUNED_MODEL_NAME = f\"{OUTPUT_PATH}/{MODEL_NAME}_fine-tuned\"\n",
+    "\n",
+    "\n",
+    "model.save_pretrained(FINETUNED_MODEL_NAME, saved_model=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We just save the fine-tuned model as a hf checkpoint. However, to import it to Spark NLP we need to modify the signature of the model. As previously mentioned, we create sentence embeddings by pooling the token embeddings. We define a new model signature, which includes the  `mean_pooling` operation and save the custom model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mean_pooling(token_embeddings, attention_mask):\n",
+    "    input_mask_expanded = tf.cast(\n",
+    "        tf.repeat(\n",
+    "            tf.expand_dims(attention_mask, -1),\n",
+    "            repeats=token_embeddings.shape[-1],\n",
+    "            axis=-1,\n",
+    "        ),\n",
+    "        tf.float32,\n",
+    "    )\n",
+    "    return tf.reduce_sum(\n",
+    "        token_embeddings * input_mask_expanded, axis=1\n",
+    "    ) / tf.clip_by_value(\n",
+    "        tf.reduce_sum(input_mask_expanded, axis=1),\n",
+    "        clip_value_min=1e-9,\n",
+    "        clip_value_max=4096,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# Define TF Signature\n",
+    "@tf.function(\n",
+    "    input_signature=[\n",
+    "        {\n",
+    "            \"input_ids\": tf.TensorSpec((None, None), tf.int32, name=\"input_ids\"),\n",
+    "            \"attention_mask\": tf.TensorSpec(\n",
+    "                (None, None), tf.int32, name=\"attention_mask\"\n",
+    "            ),\n",
+    "            \"token_type_ids\": tf.TensorSpec(\n",
+    "                (None, None), tf.int32, name=\"token_type_ids\"\n",
+    "            ),\n",
+    "        }\n",
+    "    ]\n",
+    ")\n",
+    "def serving_fn(input):\n",
+    "    outputs = model(input, output_hidden_states=True)\n",
+    "    # compute sentence embedding by averaging token embeddings\n",
+    "    pooler_output = mean_pooling(outputs.hidden_states[-1], input[\"attention_mask\"])\n",
+    "    # compute sentence embedding by taking the built in pooler output,\n",
+    "    # which currently is actually the CLS embedding. This doesn't work well,\n",
+    "    # so avoid using it\n",
+    "    # pooled_output = outputs.pooler_output\n",
+    "    return {\"pooler_output\": pooler_output}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:Found untraced functions such as embeddings_layer_call_fn, embeddings_layer_call_and_return_conditional_losses, encoder_layer_call_fn, encoder_layer_call_and_return_conditional_losses, predictions_layer_call_fn while saving (showing 5 of 424). These functions will not be directly callable after loading.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save model to local directory\n",
+    "\n",
+    "model.save_pretrained(\n",
+    "    \"{}_w_sign\".format(FINETUNED_MODEL_NAME),\n",
+    "    saved_model=True,\n",
+    "    signatures={\"serving_default\": serving_fn},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "FINETUNED_MODEL_NAME_w_sign = f\"{FINETUNED_MODEL_NAME}_w_sign\"\n",
+    "\n",
+    "!cp {OUTPUT_PATH}/{MODEL_NAME}_tokenizer/vocab.txt {FINETUNED_MODEL_NAME_w_sign}/saved_model/1/assets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing the model into Spark NLP\n",
+    "\n",
+    "It's best to restart the runtime again here, so we don't go over the RAM limit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning::Spark Session already created, some configs may not take.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <div>\n",
+       "                <p><b>SparkSession - in-memory</b></p>\n",
+       "                \n",
+       "        <div>\n",
+       "            <p><b>SparkContext</b></p>\n",
+       "\n",
+       "            <p><a href=\"http://d784806eb417:4040\">Spark UI</a></p>\n",
+       "\n",
+       "            <dl>\n",
+       "              <dt>Version</dt>\n",
+       "                <dd><code>v3.2.3</code></dd>\n",
+       "              <dt>Master</dt>\n",
+       "                <dd><code>local[*]</code></dd>\n",
+       "              <dt>AppName</dt>\n",
+       "                <dd><code>Spark NLP</code></dd>\n",
+       "            </dl>\n",
+       "        </div>\n",
+       "        \n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<pyspark.sql.session.SparkSession at 0x7eefc0703940>"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sparknlp\n",
+    "\n",
+    "from sparknlp.annotator import *\n",
+    "from pyspark.ml import Pipeline\n",
+    "from pyspark.sql import functions as F\n",
+    "\n",
+    "spark = sparknlp.start()\n",
+    "\n",
+    "spark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "OUTPUT_PATH = \"/content/sbert_tf\"\n",
+    "MODEL_NAME = \"bert-base-cased\"\n",
+    "FINETUNED_MODEL_NAME = f\"{OUTPUT_PATH}/{MODEL_NAME}_fine-tuned\"\n",
+    "\n",
+    "sent_bert = (\n",
+    "    BertSentenceEmbeddings.loadSavedModel(\n",
+    "        f\"{FINETUNED_MODEL_NAME}_w_sign/saved_model/1\", spark\n",
+    "    )\n",
+    "    .setInputCols(\"sentence\")\n",
+    "    .setOutputCol(\"bert_sentence\")\n",
+    "    .setCaseSensitive(True)\n",
+    "    .setDimension(768)\n",
+    "    .setStorageRef(\"sent_bert_base_cased\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sent_bert.write().overwrite().save(\"./{}_fine-tuned_spark_nlp\".format(MODEL_NAME))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "document_assembler = DocumentAssembler().setInputCol(\"text\").setOutputCol(\"document\")\n",
+    "\n",
+    "sentence_detector = (\n",
+    "    SentenceDetector().setInputCols([\"document\"]).setOutputCol(\"sentence\")\n",
+    ")\n",
+    "\n",
+    "embeddings = (\n",
+    "    BertSentenceEmbeddings.load(\"./{}_fine-tuned_spark_nlp\".format(MODEL_NAME))\n",
+    "    .setInputCols(\"sentence\")\n",
+    "    .setOutputCol(\"sentence_embeddings\")\n",
+    ")\n",
+    "\n",
+    "nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, embeddings])\n",
+    "\n",
+    "text = [[\"I hate cancer\"], [\"Antibiotics aren't painkiller\"]]\n",
+    "\n",
+    "data = spark.createDataFrame(text).toDF(\"text\")\n",
+    "\n",
+    "result = nlp_pipeline.fit(data).transform(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----------------------------+----------------------------------------------------------------------------------------------------+\n",
+      "|                     sentence|                                                                                          embeddings|\n",
+      "+-----------------------------+----------------------------------------------------------------------------------------------------+\n",
+      "|                I hate cancer|[0.6494873, 0.073490426, -0.29895884, -0.009830964, -0.09348484, -0.039925538, 0.3101672, 0.02736...|\n",
+      "|Antibiotics aren't painkiller|[0.28350386, -0.09607246, 0.11028457, 0.36982596, -0.1297523, -0.2121249, -0.3344884, 0.008855367...|\n",
+      "+-----------------------------+----------------------------------------------------------------------------------------------------+\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "result.select(\n",
+    "    F.explode(\n",
+    "        F.arrays_zip(result.sentence.result, result.sentence_embeddings.embeddings)\n",
+    "    ).alias(\"cols\")\n",
+    ").select(\n",
+    "    F.expr(\"cols['0']\").alias(\"sentence\"), F.expr(\"cols['1']\").alias(\"embeddings\")\n",
+    ").show(\n",
+    "    truncate=100\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inference: Comparing the fine-tuned and the orginal model\n",
+    "\n",
+    "We can now compare the embeddings, between the base model and the fine-tuned model. For this we can use the cosine similarity as a measure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "document_assembler = DocumentAssembler().setInputCol(\"text\").setOutputCol(\"document\")\n",
+    "\n",
+    "sentence_detector = (\n",
+    "    SentenceDetector().setInputCols([\"document\"]).setOutputCol(\"sentence\")\n",
+    ")\n",
+    "\n",
+    "embeddings_fine_tuned = (\n",
+    "    BertSentenceEmbeddings.load(\"./{}_fine-tuned_spark_nlp\".format(MODEL_NAME))\n",
+    "    .setInputCols(\"sentence\")\n",
+    "    .setOutputCol(\"sentence_embeddings_finetuned\")\n",
+    ")\n",
+    "\n",
+    "embeddings_original = (\n",
+    "    BertSentenceEmbeddings.load(\"./{}_spark_nlp\".format(MODEL_NAME))\n",
+    "    .setInputCols(\"sentence\")\n",
+    "    .setOutputCol(\"sentence_embeddings_original\")\n",
+    ")\n",
+    "\n",
+    "\n",
+    "nlp_pipeline = Pipeline(\n",
+    "    stages=[\n",
+    "        document_assembler,\n",
+    "        sentence_detector,\n",
+    "        embeddings_fine_tuned,\n",
+    "        embeddings_original,\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyspark.sql.functions import monotonically_increasing_id\n",
+    "\n",
+    "text = [[\"I hate cancer\"], [\"Antibiotics aren't painkiller\"]]\n",
+    "\n",
+    "data = spark.createDataFrame(text).toDF(\"text\")\n",
+    "\n",
+    "data = data.coalesce(1).withColumn(\"index\", monotonically_increasing_id())\n",
+    "\n",
+    "result = nlp_pipeline.fit(data).transform(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----+-----------------------------+--------------------------------------------------+--------------------------------------------------+\n",
+      "|index|                     sentence|                     sentence_embeddings_finetuned|                      sentence_embeddings_original|\n",
+      "+-----+-----------------------------+--------------------------------------------------+--------------------------------------------------+\n",
+      "|    0|                I hate cancer|[0.6494875, 0.07349018, -0.29895863, -0.0098310...|[0.67558324, 0.052480347, -0.2677792, -0.026199...|\n",
+      "|    1|Antibiotics aren't painkiller|[0.28350395, -0.096072316, 0.11028453, 0.369825...|[0.34588462, -0.06992395, 0.15711544, 0.3646099...|\n",
+      "+-----+-----------------------------+--------------------------------------------------+--------------------------------------------------+\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pyspark.sql.functions as F\n",
+    "\n",
+    "df = result.select(\n",
+    "    \"index\",\n",
+    "    F.explode(\n",
+    "        F.arrays_zip(\n",
+    "            result.sentence.result,\n",
+    "            result.sentence_embeddings_finetuned.embeddings,\n",
+    "            result.sentence_embeddings_original.embeddings,\n",
+    "        )\n",
+    "    ).alias(\"cols\"),\n",
+    ").select(\n",
+    "    \"index\",\n",
+    "    F.expr(\"cols['0']\").alias(\"sentence\"),\n",
+    "    F.expr(\"cols['1']\").alias(\"sentence_embeddings_finetuned\"),\n",
+    "    F.expr(\"cols['2']\").alias(\"sentence_embeddings_original\"),\n",
+    ")\n",
+    "df.show(truncate=50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
+      "|sentence     |sentence_embeddings_original                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |\n",
+      "+-------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
+      "|I hate cancer|[0.67558324, 0.052480347, -0.2677792, -0.026199222, -0.0686846, -0.03861774, 0.29574794, 0.02090778, 0.2397081, -0.8842683, 0.2086796, 0.45777363, -0.2844552, 0.32825053, -0.8087675, 0.07840201, -0.10437469, 0.59855855, 0.2985413, 0.16562526, 0.15523097, 0.1621953, 0.15382598, -0.26807594, 0.13527563, -0.080688015, 0.56401426, 0.6133276, -0.28981248, 0.20128974, 0.2895462, -3.5020112E-4, -0.03259187, 0.30807486, -0.38619968, 0.021715393, -0.2763726, -0.14539608, -0.18481253, -0.15936127, -0.28518137, 0.047853373, 0.64794004, -0.2561886, 0.26201198, -0.2540049, -0.26061612, 0.102657095, -0.5721179, 0.09564935, 0.0053400113, -0.3423562, 0.08338155, 0.1554383, 0.33479747, -0.12064157, -0.4952808, 0.18489511, -0.48605546, 0.18152533, -0.13310246, -0.09355955, 0.46414432, 0.03483261, -0.32441312, 0.093772724, 0.18530712, -0.11479372, -0.19833173, -0.106865644, 0.0043842793, 0.2347014, 0.25414702, 0.18336138, 0.07742501, -0.42139477, 0.21043329, -0.11350949, 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+      "|sentence     |sentence_embeddings_finetuned                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     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+      "+-------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
+      "\n",
+      "cos_sim: 0.9959553324488427\n",
+      "\n",
+      "\n",
+      "\n",
+      "+-----------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
+      "|sentence                     |sentence_embeddings_original                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |\n",
+      "+-----------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
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+      "|sentence                     |sentence_embeddings_finetuned                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     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+      "+-----------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
+      "\n",
+      "cos_sim: 0.994821982746426\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics.pairwise import cosine_similarity\n",
+    "import numpy as np\n",
+    "\n",
+    "pdf = df.toPandas()\n",
+    "\n",
+    "X = np.stack(pdf.sentence_embeddings_original.values)\n",
+    "Y = np.stack(pdf.sentence_embeddings_finetuned.values)\n",
+    "sk_sim = cosine_similarity(X, Y)\n",
+    "\n",
+    "\n",
+    "for i in range(df.count()):\n",
+    "    df.filter(result.index == i).select(\n",
+    "        \"sentence\", \"sentence_embeddings_original\"\n",
+    "    ).show(truncate=False)\n",
+    "    df.filter(result.index == i).select(\n",
+    "        \"sentence\", \"sentence_embeddings_finetuned\"\n",
+    "    ).show(truncate=False)\n",
+    "    print(f\"cos_sim: {sk_sim[i,i]}\\n\\n\\n\")"
+   ]
+  }
+ ],
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