diff --git a/examples/python/transformers/HuggingFace_in_Spark_NLP_AlbertForZeroShotClassification.ipynb b/examples/python/transformers/HuggingFace_in_Spark_NLP_AlbertForZeroShotClassification.ipynb
new file mode 100644
index 00000000000000..39296e1081a74e
--- /dev/null
+++ b/examples/python/transformers/HuggingFace_in_Spark_NLP_AlbertForZeroShotClassification.ipynb
@@ -0,0 +1,3053 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jyP44g5ieZsq"
+   },
+   "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/HuggingFace_in_Spark_NLP_AlbertForZeroShotClassification.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "W8r6iDKceZs7"
+   },
+   "source": [
+    "## Import AlbertForZeroShotClassification models from HuggingFace 🤗  into Spark NLP 🚀\n",
+    "\n",
+    "Let's keep in mind a few things before we start 😊\n",
+    "\n",
+    "- This feature is only in `Spark NLP 5.4.2` and after. So please make sure you have upgraded to the latest Spark NLP release\n",
+    "- You can import ALBERT models trained/fine-tuned for sequence classification via `AlbertForSequenceClassification` or `TFAlbertForSequenceClassification`. These models are usually under `Text Classification` category and have `albert` in their labels\n",
+    "- Reference: [TFAlbertForSequenceClassification](https://huggingface.co/docs/transformers/model_doc/albert#transformers.TFAlbertForSequenceClassification)\n",
+    "- Some [example models](https://huggingface.co/models?filter=albert&pipeline_tag=text-classification)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "oh2BmRddeZtA"
+   },
+   "source": [
+    "## Export and Save HuggingFace model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "I3OaTwVYeZtC"
+   },
+   "source": [
+    "- Let's install `HuggingFace` and `TensorFlow`. You don't need `TensorFlow` to be installed for Spark NLP, however, we need it to load and save models from HuggingFace.\n",
+    "- We lock TensorFlow on `2.11.0` version and Transformers on `4.25.1`. This doesn't mean it won't work with the future releases, but we wanted you to know which versions have been tested successfully.\n",
+    "- Albert uses SentencePiece, so we will have to install that as well"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "PFN7a31JeZtE",
+    "outputId": "2d8b80dc-91af-4dd1-fe9d-29751b994458"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.8/8.8 MB\u001b[0m \u001b[31m13.8 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[31m2.4 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[31m71.0 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[31m56.3 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[31m116.0 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[31m31.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m97.2 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[31m102.3 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[31m45.0 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",
+      "cudf-cu12 24.4.1 requires protobuf<5,>=3.20, but you have protobuf 3.19.6 which is incompatible.\n",
+      "google-cloud-bigquery-connection 1.15.5 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
+      "google-cloud-bigtable 2.25.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
+      "google-cloud-functions 1.16.5 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
+      "google-cloud-iam 2.15.2 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
+      "google-cloud-language 2.13.4 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
+      "google-cloud-pubsub 2.23.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
+      "google-cloud-resource-manager 1.12.5 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
+      "google-cloud-translate 3.15.5 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
+      "googleapis-common-protos 1.63.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0.dev0,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
+      "grpc-google-iam-v1 0.13.1 requires protobuf!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\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 4.9.6 requires protobuf>=3.20, but you have protobuf 3.19.6 which is incompatible.\n",
+      "tensorflow-metadata 1.15.0 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 3.19.6 which is incompatible.\n",
+      "tf-keras 2.17.0 requires tensorflow<2.18,>=2.17, but you have tensorflow 2.11.0 which is incompatible.\u001b[0m\u001b[31m\n",
+      "\u001b[0m"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install -q transformers==4.39.3 tensorflow==2.11.0 sentencepiece"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "AKsQCgipeZtI"
+   },
+   "source": [
+    "- HuggingFace comes with a native `saved_model` feature inside `save_pretrained` function for TensorFlow based models. We will use that to save it as TF `SavedModel`.\n",
+    "- We'll use [DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT'](https://huggingface.co/mohsenfayyaz/albert-base-v2-toxicity) model from HuggingFace as an example\n",
+    "- In addition to `TFAlbertForSequenceClassification` we also need to save the `AlbertTokenizer`. This is the same for every model, these are assets needed for tokenization inside Spark NLP."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 379,
+     "referenced_widgets": [
+      "87015e2373034217a1b8ac76441caf49",
+      "411fd89d1a31427f9bf9e35b38c2f420",
+      "3f5840d43cc54c90ad334c1c28116e29",
+      "4563668896c84756b3eadd789dc759d7",
+      "0920e6780a0d4a1bac328451ab722dc3",
+      "ceb2224ee447481d87cfea97517fbca6",
+      "a5ed208cf9854ed5bdb6f3b36168456e",
+      "a15f448686e945faa99f2316dffe177c",
+      "2c1158f9725141df9c996da47ae030d0",
+      "cdcf470194a14f8f9149146c4dcb567c",
+      "243d96a25a5447faa1f145c7af93ae22",
+      "db12325d7df341c9a5d8c52b9486b6c0",
+      "bcb8fa3afe3c498090af08ec31d3bf49",
+      "9fe306b8d7014a45b5a9ddccd5c09380",
+      "1483578c50ad492a8b5f57927c994410",
+      "8f6274003b304dc2a15ecb3ebe3961b7",
+      "803a8344b2344a7691851f117dd4ac06",
+      "225bb44c41d24f3ebf6e27952a5fd61b",
+      "dd975e4a7cb34eca8a97b3e02f502aa5",
+      "d45e2ac8c5494450abeb8b5d0a6a9297",
+      "cba34126485e4bd984347ffe9cf0f387",
+      "35434d241da946f9b48c584eeaa1e61c"
+     ]
+    },
+    "id": "7n9_7HNoeZtL",
+    "outputId": "aef3c068-340c-436a-88cb-cc2563c34a29"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "try downloading TF weights\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
+      "  warnings.warn(\n",
+      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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": "87015e2373034217a1b8ac76441caf49",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/1.77k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "try downloading PyTorch weights\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db12325d7df341c9a5d8c52b9486b6c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "pytorch_model.bin:   0%|          | 0.00/891M [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 TFAlbertForSequenceClassification: ['albert.embeddings.position_ids']\n",
+      "- This IS expected if you are initializing TFAlbertForSequenceClassification 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 TFAlbertForSequenceClassification 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 TFAlbertForSequenceClassification 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 TFAlbertForSequenceClassification for predictions without further training.\n",
+      "WARNING:absl:Found untraced functions such as serving, embeddings_layer_call_fn, embeddings_layer_call_and_return_conditional_losses, encoder_layer_call_fn, encoder_layer_call_and_return_conditional_losses while saving (showing 5 of 41). These functions will not be directly callable after loading.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import TFAlbertForSequenceClassification, AlbertTokenizer\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "MODEL_NAME = 'DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT'\n",
+    "\n",
+    "# just in case if there is no TF/Keras file provided in the model\n",
+    "# we can just use `from_pt` and convert PyTorch to TensorFlow\n",
+    "try:\n",
+    "  print('try downloading TF weights')\n",
+    "  model = TFAlbertForSequenceClassification.from_pretrained(MODEL_NAME)\n",
+    "except:\n",
+    "  print('try downloading PyTorch weights')\n",
+    "  model = TFAlbertForSequenceClassification.from_pretrained(MODEL_NAME, from_pt=True)\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((None, None), tf.int32, name=\"attention_mask\"),\n",
+    "          \"token_type_ids\": tf.TensorSpec((None, None), tf.int32, name=\"token_type_ids\"),\n",
+    "      }\n",
+    "  ]\n",
+    ")\n",
+    "def serving_fn(input):\n",
+    "    return model(input)\n",
+    "\n",
+    "model.save_pretrained(\"./{}\".format(MODEL_NAME), saved_model=True, signatures={\"serving_default\": serving_fn})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mPClpTYDeZtR"
+   },
+   "source": [
+    "Let's have a look inside these two directories and see what we are dealing with:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "UGfFnfdNeZtU",
+    "outputId": "4e31bc90-9c6b-4492-e1bf-ea199a05919c"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 869888\n",
+      "-rw-r--r-- 1 root root      1787 Aug  7 18:39 config.json\n",
+      "drwxr-xr-x 3 root root      4096 Aug  7 18:39 saved_model\n",
+      "-rw-r--r-- 1 root root 890751296 Aug  7 18:39 tf_model.h5\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -l {MODEL_NAME}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "lWpp8N-VeZtW",
+    "outputId": "429764d1-c6e5-4c21-84ec-ba3f5beb7133"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 9500\n",
+      "drwxr-xr-x 2 root root    4096 Aug  7 18:39 assets\n",
+      "-rw-r--r-- 1 root root      53 Aug  7 18:39 fingerprint.pb\n",
+      "-rw-r--r-- 1 root root   26815 Aug  7 18:39 keras_metadata.pb\n",
+      "-rw-r--r-- 1 root root 9683747 Aug  7 18:39 saved_model.pb\n",
+      "drwxr-xr-x 2 root root    4096 Aug  7 18:39 variables\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -l {MODEL_NAME}/saved_model/1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "bEL3eXC2mFwo"
+   },
+   "source": [
+    "We are using based model for the tokenizer because the model `DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT` does not have sentencepiece"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 234,
+     "referenced_widgets": [
+      "d48bdae428d449fc8f484239845b4f3e",
+      "e1e6c5f08ef74408942d543689d3f1d8",
+      "d13075d18a5c4806917ac20a4346c7d5",
+      "5d78cd5e181c44f6905e633ab192623f",
+      "87f6295ed6af476e95ac9de6ea842d07",
+      "bcc9391220ba4e9ab8200c7d2d6ed700",
+      "ad9d69e29f824ed68d3b52fee87602ed",
+      "3ed35baae8d04d04812006567801d6c5",
+      "7562c283ba40488d9692d54280003006",
+      "a86bdddb9c9349c1a89181e67be4ffed",
+      "d867c8d064104b45ac2c97efc9a1a115",
+      "c25d44847df146f185237969cdaabb44",
+      "5fd989e52ab5451bad09a7ddf2eeaf4e",
+      "50b985a35e524cb79017b836cf4a080a",
+      "7aa0d63cfcd54d0cb34d0b5276ecb0c9",
+      "ddd8df80c4a04f56852b6e9caf257e74",
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+      "ff1c9d4f825f406aba6f4d110e913fc2"
+     ]
+    },
+    "id": "Fd3HtEw-lR9t",
+    "outputId": "7a592822-d35a-49d3-f964-04e979c809e6"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d48bdae428d449fc8f484239845b4f3e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/25.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c25d44847df146f185237969cdaabb44",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "spiece.model:   0%|          | 0.00/760k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7be5582eba2d43bdbc4bacd1dbfe9256",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/1.31M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11e89cc832fd4f8787d1f46a104e457a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/710 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tokenizer loaded successfully!\n",
+      "Tokenizer saved successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import AlbertTokenizer\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# Try to load the tokenizer\n",
+    "try:\n",
+    "    tokenizer = AlbertTokenizer.from_pretrained('albert-xxlarge-v2')\n",
+    "    print(\"Tokenizer loaded successfully!\")\n",
+    "except OSError as e:\n",
+    "    print(f\"Error loading tokenizer: {e}\")\n",
+    "\n",
+    "# Try to save the tokenizer\n",
+    "try:\n",
+    "    tokenizer.save_pretrained('./{}_tokenizer/'.format(MODEL_NAME))\n",
+    "    print(\"Tokenizer saved successfully!\")\n",
+    "except Exception as e:\n",
+    "    print(f\"Error saving tokenizer: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "tG0uJu1SeZtX",
+    "outputId": "c3761f7e-1b86-438b-fe0f-77c9737d2aca"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 752\n",
+      "-rw-r--r-- 1 root root    286 Aug  7 18:39 special_tokens_map.json\n",
+      "-rw-r--r-- 1 root root 760289 Aug  7 18:39 spiece.model\n",
+      "-rw-r--r-- 1 root root   1246 Aug  7 18:39 tokenizer_config.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -l {MODEL_NAME}_tokenizer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QoyUgF3ZeZtZ"
+   },
+   "source": [
+    "- as you can see, we need the SavedModel from `saved_model/1/` path\n",
+    "- we also be needing `spiece.model` file from the tokenizer\n",
+    "- all we need is to copy `spiece.model` file into `saved_model/1/assets` which Spark NLP will look for\n",
+    "- in addition to vocabs, we also need `labels` and their `ids` which is saved inside the model's config. We will save this inside `labels.txt`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "q0_9K337eZta"
+   },
+   "outputs": [],
+   "source": [
+    "asset_path = '{}/saved_model/1/assets'.format(MODEL_NAME)\n",
+    "\n",
+    "# let's copy spiece.model file to saved_model/1/assets\n",
+    "!cp {MODEL_NAME}_tokenizer/spiece.model {asset_path}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "Y6TiWYJGeZtb"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import json\n",
+    "\n",
+    "# Define human-readable labels (based on your example)\n",
+    "human_readable_labels = [\"entailment\", \"contradict\"]\n",
+    "\n",
+    "# Get the auto-generated label mapping\n",
+    "labels = model.config.label2id\n",
+    "\n",
+    "# Determine the maximum index in id2label\n",
+    "max_index = max(labels.values())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "aX-TL8Dan9U_"
+   },
+   "outputs": [],
+   "source": [
+    "# Map auto-generated labels to human-readable labels\n",
+    "mapped_labels = []\n",
+    "for idx in range(max_index + 1):\n",
+    "    if idx < len(human_readable_labels):\n",
+    "        mapped_labels.append(human_readable_labels[idx])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Jyq3aopBoAIs",
+    "outputId": "8045d2db-96e0-4f56-d62e-ac4956b365e1"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['entailment', 'contradict']"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mapped_labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "id": "VHEF56vQn6E0"
+   },
+   "outputs": [],
+   "source": [
+    "with open(asset_path+'/labels.txt', 'w') as f:\n",
+    "    f.write('\\n'.join(mapped_labels))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "NZPzuWglrMB0",
+    "outputId": "7720e34c-faf5-4a49-9b84-c659a4566644"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entailment\n",
+      "contradict"
+     ]
+    }
+   ],
+   "source": [
+    "!cat {asset_path}'/labels.txt'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "h33oq-zteZtc"
+   },
+   "source": [
+    "Voila! We have our `vocab.txt` and `labels.txt` inside assets directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "s18JBtQreZtd",
+    "outputId": "52967386-14f9-4de5-b3f7-76fe36228143"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 748\n",
+      "-rw-r--r-- 1 root root     21 Aug  7 18:39 labels.txt\n",
+      "-rw-r--r-- 1 root root 760289 Aug  7 18:39 spiece.model\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -l {MODEL_NAME}/saved_model/1/assets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AVzljs1yohoW",
+    "outputId": "4e129e25-53bc-4146-e25b-c4b954f4081e"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT/saved_model/1:\n",
+      "total 9500\n",
+      "drwxr-xr-x 2 root root    4096 Aug  7 18:39 assets\n",
+      "-rw-r--r-- 1 root root      53 Aug  7 18:39 fingerprint.pb\n",
+      "-rw-r--r-- 1 root root   26815 Aug  7 18:39 keras_metadata.pb\n",
+      "-rw-r--r-- 1 root root 9683747 Aug  7 18:39 saved_model.pb\n",
+      "drwxr-xr-x 2 root root    4096 Aug  7 18:39 variables\n",
+      "\n",
+      "DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT/saved_model/1/assets:\n",
+      "total 748\n",
+      "-rw-r--r-- 1 root root     21 Aug  7 18:39 labels.txt\n",
+      "-rw-r--r-- 1 root root 760289 Aug  7 18:39 spiece.model\n",
+      "\n",
+      "DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT/saved_model/1/variables:\n",
+      "total 869864\n",
+      "-rw-r--r-- 1 root root 890729740 Aug  7 18:39 variables.data-00000-of-00001\n",
+      "-rw-r--r-- 1 root root      1711 Aug  7 18:39 variables.index\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lR {MODEL_NAME}/saved_model/1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_TS52yqneZte"
+   },
+   "source": [
+    "## Import and Save AlbertForZeroShotClassification in Spark NLP\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UyXSx_VyeZtf"
+   },
+   "source": [
+    "- Let's install and setup Spark NLP in Google Colab\n",
+    "- This part is pretty easy via our simple script"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "id": "vjVo8i2ZeZtf"
+   },
+   "outputs": [],
+   "source": [
+    "! wget http://setup.johnsnowlabs.com/colab.sh -O - | bash"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "alSHaJ4veZtg"
+   },
+   "source": [
+    "Let's start Spark with Spark NLP included via our simple `start()` function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "UTkJgtxfeZth",
+    "outputId": "b0d222a9-d420-43de-c8cb-44bdf90f51b3"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/lib/python3.10/subprocess.py:1796: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
+      "  self.pid = _posixsubprocess.fork_exec(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Apache Spark version: 3.4.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# import sparknlp\n",
+    "# let's start Spark with Spark NLP\n",
+    "spark = sparknlp.start()\n",
+    "\n",
+    "print(\"Apache Spark version: {}\".format(spark.version))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "w0W4vTAFeZti"
+   },
+   "source": [
+    "- Let's use `loadSavedModel` functon in `AlbertForZeroShotClassification` which allows us to load TensorFlow model in SavedModel format\n",
+    "- Most params can be set later when you are loading this model in `AlbertForZeroShotClassification` in runtime like `setMaxSentenceLength`, so don't worry what you are setting them now\n",
+    "- `loadSavedModel` accepts two params, first is the path to the TF SavedModel. The second is the SparkSession that is `spark` variable we previously started via `sparknlp.start()`\n",
+    "- NOTE: `loadSavedModel` accepts local paths in addition to distributed file systems such as `HDFS`, `S3`, `DBFS`, etc. This feature was introduced in Spark NLP 4.2.2 release. Keep in mind the best and recommended way to move/share/reuse Spark NLP models is to use `write.save` so you can use `.load()` from any file systems natively.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "id": "vCz_i1fueZti"
+   },
+   "outputs": [],
+   "source": [
+    "from sparknlp.annotator import *\n",
+    "\n",
+    "zero_shot_classifier = AlbertForZeroShotClassification\\\n",
+    "  .loadSavedModel('{}/saved_model/1'.format(MODEL_NAME), spark)\\\n",
+    "  .setInputCols([\"document\",'token'])\\\n",
+    "  .setOutputCol(\"class\")\\\n",
+    "  .setCaseSensitive(False)\\\n",
+    "  .setMaxSentenceLength(128)\\\n",
+    "  .setCandidateLabels([\"urgent\", \"mobile\", \"technology\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "z5ED2fCWeZtj"
+   },
+   "source": [
+    "- Let's save it on disk so it is easier to be moved around and also be used later via `.load` function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "id": "BkQQDc33eZtk"
+   },
+   "outputs": [],
+   "source": [
+    "zero_shot_classifier.write().overwrite().save(\"./{}_spark_nlp\".format(MODEL_NAME))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Q_9SeH03eZtk"
+   },
+   "source": [
+    "Let's clean up stuff we don't need anymore"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "id": "2NxUp-zkeZtl"
+   },
+   "outputs": [],
+   "source": [
+    "!rm -rf {MODEL_NAME}_tokenizer {MODEL_NAME}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "w7tCcHLVeZtl"
+   },
+   "source": [
+    "Awesome 😎  !\n",
+    "\n",
+    "This is your AlbertForZeroShotClassification model from HuggingFace 🤗  loaded and saved by Spark NLP 🚀"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JqFvt07ceZtm",
+    "outputId": "13a92ba7-a531-431f-ec2d-8547438931e7"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 880148\n",
+      "-rw-r--r-- 1 root root 900495299 Aug  7 18:44 albert_classification_tensorflow\n",
+      "-rw-r--r-- 1 root root    760289 Aug  7 18:44 albert_spp\n",
+      "drwxr-xr-x 4 root root      4096 Aug  7 18:44 fields\n",
+      "drwxr-xr-x 2 root root      4096 Aug  7 18:44 metadata\n"
+     ]
+    }
+   ],
+   "source": [
+    "! ls -l {MODEL_NAME}_spark_nlp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "H1OF93ZaeZtn"
+   },
+   "source": [
+    "Now let's see how we can use it on other machines, clusters, or any place you wish to use your new and shiny AlbertForZeroShotClassification model 😊"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "id": "F7ciZBFNeZto"
+   },
+   "outputs": [],
+   "source": [
+    "sequenceClassifier_loaded = AlbertForZeroShotClassification.load(\"./{}_spark_nlp\".format(MODEL_NAME))\\\n",
+    "  .setInputCols([\"document\",'token'])\\\n",
+    "  .setOutputCol(\"multi_class\") \\\n",
+    "  .setCandidateLabels([\"urgent\", \"mobile\", \"technology\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "j4i_z4pUeZtq"
+   },
+   "source": [
+    "This is how you can use your loaded classifier model in Spark NLP 🚀 pipeline:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Dff0iOOJeZtr",
+    "outputId": "481a4c6a-e841-4006-f95e-1d274dfa3ad1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+--------+\n",
+      "|                text|  result|\n",
+      "+--------------------+--------+\n",
+      "|I have a problem ...|[urgent]|\n",
+      "+--------------------+--------+\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pyspark.ml import Pipeline\n",
+    "\n",
+    "from sparknlp.base import *\n",
+    "from sparknlp.annotator import *\n",
+    "\n",
+    "document_assembler = DocumentAssembler() \\\n",
+    "    .setInputCol('text') \\\n",
+    "    .setOutputCol('document')\n",
+    "\n",
+    "tokenizer = Tokenizer() \\\n",
+    "    .setInputCols(['document']) \\\n",
+    "    .setOutputCol('token')\n",
+    "\n",
+    "pipeline = Pipeline(stages=[\n",
+    "    document_assembler,\n",
+    "    tokenizer,\n",
+    "    sequenceClassifier_loaded\n",
+    "])\n",
+    "\n",
+    "# couple of simple examples\n",
+    "example = spark.createDataFrame([[\"I have a problem with my iphone that needs to be resolved asap!!\"]]).toDF(\"text\")\n",
+    "\n",
+    "result = pipeline.fit(example).transform(example)\n",
+    "\n",
+    "# result is a DataFrame\n",
+    "result.select(\"text\", \"multi_class.result\").show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "avSm2DQ1eZtt"
+   },
+   "source": [
+    "That's it! You can now go wild and use hundreds of `AlbertForZeroShotClassification` models from HuggingFace 🤗 in Spark NLP 🚀\n"
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diff --git a/examples/python/transformers/onnx/HuggingFace_ONNX_in_Spark_NLP_AlbertForZeroShotClassification.ipynb b/examples/python/transformers/onnx/HuggingFace_ONNX_in_Spark_NLP_AlbertForZeroShotClassification.ipynb
new file mode 100644
index 00000000000000..bb03a4dcfda077
--- /dev/null
+++ b/examples/python/transformers/onnx/HuggingFace_ONNX_in_Spark_NLP_AlbertForZeroShotClassification.ipynb
@@ -0,0 +1,4048 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "myRLo4DoOXMv"
+   },
+   "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/onnx/HuggingFace_ONNX_in_Spark_NLP_AlbertForZeroShotClassification.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kbcLY-vUOXMx"
+   },
+   "source": [
+    "## Import ONNX AlbertForZeroShotClassification models from HuggingFace 🤗  into Spark NLP 🚀\n",
+    "\n",
+    "Let's keep in mind a few things before we start 😊\n",
+    "\n",
+    "- ONNX support was introduced in  `Spark NLP 5.0.0`, enabling high performance inference for models.\n",
+    "- `AlbertForZeroShotClassification` is only available since in `Spark NLP 5.4.2` and after. So please make sure you have upgraded to the latest Spark NLP release\n",
+    "- You can import ALBERT models trained/fine-tuned for sequence classification via `AlbertForSequenceClassification` or `TFAlbertForSequenceClassification`. These models are usually under `Sequence Classification` category and have `camembert` in their labels\n",
+    "- Reference: [TFAlbertForSequenceClassification](https://huggingface.co/docs/transformers/model_doc/camembert#transformers.TFCamembertForSequenceClassification)\n",
+    "- Some [example models](https://huggingface.co/models?other=camembert&pipeline_tag=token-classification)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "p6AcYDNmOXMy"
+   },
+   "source": [
+    "## Export and Save HuggingFace model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "bdBT4GeOOXMy"
+   },
+   "source": [
+    "- Let's install `transformers` package with the `onnx` extension and it's dependencies. You don't need `onnx` to be installed for Spark NLP, however, we need it to load and save models from HuggingFace.\n",
+    "- We lock `transformers` on version `4.34.1`. This doesn't mean it won't work with the future releases, but we wanted you to know which versions have been tested successfully.\n",
+    "- CamembertTokenizer requires the `SentencePiece` library, so we install that as well"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "X_2GFHYJOXMz",
+    "outputId": "4abc0f24-a553-4d53-d648-a761e85cd633"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.7/43.7 kB\u001b[0m \u001b[31m2.7 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[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.7/212.7 kB\u001b[0m \u001b[31m20.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.0/6.0 MB\u001b[0m \u001b[31m113.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m781.3/781.3 kB\u001b[0m \u001b[31m53.0 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[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.5/55.5 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m17.9 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",
+      "cudf-cu12 24.4.1 requires protobuf<5,>=3.20, but you have protobuf 3.19.6 which is incompatible.\n",
+      "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
+      "gcsfs 2024.6.1 requires fsspec==2024.6.1, but you have fsspec 2024.5.0 which is incompatible.\n",
+      "google-cloud-bigquery-connection 1.15.5 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
+      "google-cloud-bigtable 2.25.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
+      "google-cloud-functions 1.16.5 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
+      "google-cloud-iam 2.15.2 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
+      "google-cloud-language 2.13.4 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
+      "google-cloud-pubsub 2.23.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
+      "google-cloud-resource-manager 1.12.5 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
+      "google-cloud-translate 3.15.5 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
+      "googleapis-common-protos 1.63.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0.dev0,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
+      "grpc-google-iam-v1 0.13.1 requires protobuf!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.\n",
+      "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\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 4.9.6 requires protobuf>=3.20, but you have protobuf 3.19.6 which is incompatible.\n",
+      "tensorflow-metadata 1.15.0 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 3.19.6 which is incompatible.\n",
+      "tf-keras 2.17.0 requires tensorflow<2.18,>=2.17, but you have tensorflow 2.11.0 which is incompatible.\u001b[0m\u001b[31m\n",
+      "\u001b[0m"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install -q --upgrade transformers[onnx] optimum sentencepiece tensorflow==2.11.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KwJwuD8zOXM0"
+   },
+   "source": [
+    "- HuggingFace has an extension called Optimum which offers specialized model inference, including ONNX. We can use this to import and export ONNX models with `from_pretrained` and `save_pretrained`.\n",
+    "- We'll use [DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT](https://huggingface.co/DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT)  model from HuggingFace as an example and load it as a `ORTModelForSequenceClassification`, representing an ONNX model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 405,
+     "referenced_widgets": [
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+      "1725349d5c2940c5a74fc8d4676d84e2",
+      "91e93912c6704288b1ead18fcb9aee9e",
+      "ca52d3613af14540828687ba3d9748ab",
+      "4d04cd7024a341268f759d2790836a0d",
+      "6e38416e898f429484b8399994a90bae",
+      "c921fb0d282141a3964b0f8d5c91dc4b",
+      "f737165ed69c4b199c9fac9bdd18faa8",
+      "61fbc6373e374ceca4889cfbc91cf5e1",
+      "0a81dfc4f5b64173b1577ece9bf2c2f7",
+      "b2cb083a419e4c34b64ad545d638a11b",
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+      "d95ec2867f90451195d80eb731dc651f",
+      "c89bdfb092574fb5b003adaf9bffe1c0",
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+      "6d39b93d1da246a78770497eb9403b6a"
+     ]
+    },
+    "id": "xm2lRUOxOXM0",
+    "outputId": "6c8c4b8d-1092-4842-a548-2405cf48def9"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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": "7fbb3e3d9c3b48428a2cc92e0f36eb47",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/1.77k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Framework not specified. Using pt to export the model.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5fe97bffe2744b4cbc60ce550b2503a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "pytorch_model.bin:   0%|          | 0.00/891M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce64ac856dc6472385eccab5342738c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/486 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d2a53af0aaa4501b35f7fec8ff0b273",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/2.27M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e38416e898f429484b8399994a90bae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/245 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+      "  warnings.warn(\n",
+      "Using the export variant default. Available variants are:\n",
+      "    - default: The default ONNX variant.\n",
+      "Using framework PyTorch: 2.3.1+cu121\n"
+     ]
+    }
+   ],
+   "source": [
+    "from optimum.onnxruntime import ORTModelForSequenceClassification\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "MODEL_NAME = 'DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT'\n",
+    "ONNX_MODEL = f\"onnx_models/{MODEL_NAME}\"\n",
+    "\n",
+    "ort_model = ORTModelForSequenceClassification.from_pretrained(MODEL_NAME, export=True)\n",
+    "\n",
+    "# Save the ONNX model\n",
+    "ort_model.save_pretrained(ONNX_MODEL)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "42GXDuPCOXM1"
+   },
+   "source": [
+    "Let's have a look inside this directory and see what we are dealing with:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "NTxJiuBoOXM1",
+    "outputId": "2702dfc2-daa0-4e88-f9be-4127a49616aa"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 872444\n",
+      "-rw-r--r-- 1 root root      1787 Aug  7 18:30 config.json\n",
+      "-rw-r--r-- 1 root root 891091065 Aug  7 18:30 model.onnx\n",
+      "-rw-r--r-- 1 root root       970 Aug  7 18:30 special_tokens_map.json\n",
+      "-rw-r--r-- 1 root root      1221 Aug  7 18:30 tokenizer_config.json\n",
+      "-rw-r--r-- 1 root root   2272346 Aug  7 18:30 tokenizer.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -l {ONNX_MODEL}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "VU-_uvb9TUL6"
+   },
+   "source": [
+    "We are using based model for the tokenizer because the model `DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT` does not have sentencepiece"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 180,
+     "referenced_widgets": [
+      "1b1a40671e6e453991dd1470b95b7ef5",
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+      "c4dbdb1beb704eeaacef1daad893379b",
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+     ]
+    },
+    "id": "klcWcbmhTFhC",
+    "outputId": "8cb070c3-d662-4c7b-d87a-63794e179447"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b1a40671e6e453991dd1470b95b7ef5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/25.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e0293418a1c48acadc66dd55e719c70",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "spiece.model:   0%|          | 0.00/760k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31bca4e262e444909327cc24b7626d4b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/1.31M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c557ce856164836bc6c70d9271d168b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/710 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tokenizer loaded successfully!\n",
+      "Tokenizer saved successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import TFAlbertForSequenceClassification, AlbertTokenizer\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# Try to load the tokenizer\n",
+    "try:\n",
+    "    tokenizer = AlbertTokenizer.from_pretrained('albert-xxlarge-v2')\n",
+    "    print(\"Tokenizer loaded successfully!\")\n",
+    "except OSError as e:\n",
+    "    print(f\"Error loading tokenizer: {e}\")\n",
+    "\n",
+    "# Try to save the tokenizer\n",
+    "try:\n",
+    "    tokenizer.save_pretrained(ONNX_MODEL)\n",
+    "    print(\"Tokenizer saved successfully!\")\n",
+    "except Exception as e:\n",
+    "    print(f\"Error saving tokenizer: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "rUab53MhT9dj",
+    "outputId": "2e6ef9f3-81a7-46e0-fe9d-57b18a0fbecc"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 873188\n",
+      "-rw-r--r-- 1 root root      1787 Aug  7 18:30 config.json\n",
+      "-rw-r--r-- 1 root root 891091065 Aug  7 18:30 model.onnx\n",
+      "-rw-r--r-- 1 root root       286 Aug  7 18:30 special_tokens_map.json\n",
+      "-rw-r--r-- 1 root root    760289 Aug  7 18:30 spiece.model\n",
+      "-rw-r--r-- 1 root root      1246 Aug  7 18:30 tokenizer_config.json\n",
+      "-rw-r--r-- 1 root root   2272346 Aug  7 18:30 tokenizer.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -l {ONNX_MODEL}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "id": "bx24WfBQOXM1"
+   },
+   "outputs": [],
+   "source": [
+    "!mkdir {ONNX_MODEL}/assets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lUKXGzFYOXM1"
+   },
+   "source": [
+    "- As you can see, we need to move `spiece.model` from the tokenizer to assets folder which Spark NLP will look for\n",
+    "- In addition to vocabs, we also need `labels` and their `ids` which is saved inside the model's config. We will save this inside `labels.txt`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "id": "GT-EioRiOXM1"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import json\n",
+    "\n",
+    "# Define human-readable labels (based on your example)\n",
+    "human_readable_labels = [\"entailment\", \"contradict\"]\n",
+    "\n",
+    "# Get the auto-generated label mapping\n",
+    "labels = ort_model.config.id2label\n",
+    "\n",
+    "# Determine the maximum index in id2label\n",
+    "max_index = max(labels.keys())\n",
+    "\n",
+    "# Map auto-generated labels to human-readable labels\n",
+    "mapped_labels = []\n",
+    "for idx in range(max_index + 1):\n",
+    "    if idx < len(human_readable_labels):\n",
+    "        mapped_labels.append(human_readable_labels[idx])\n",
+    "# Write the mapped labels to a text file\n",
+    "os.makedirs(os.path.join(ONNX_MODEL, 'assets'), exist_ok=True)\n",
+    "with open(os.path.join(ONNX_MODEL, 'assets', 'labels.txt'), 'w') as f:\n",
+    "    f.write('\\n'.join(mapped_labels))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "mYD9y9qDOXM2"
+   },
+   "outputs": [],
+   "source": [
+    "!mv {ONNX_MODEL}/spiece.model {ONNX_MODEL}/assets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "oTyrVcwPOXM2"
+   },
+   "source": [
+    "Voila! We have our `spiece.model` and `labels.txt` inside assets directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XTzghoZ9OXM2",
+    "outputId": "976f89f9-69c5-4755-86e1-ad998d031e7d"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "onnx_models/DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT:\n",
+      "total 872448\n",
+      "drwxr-xr-x 2 root root      4096 Aug  7 18:30 assets\n",
+      "-rw-r--r-- 1 root root      1787 Aug  7 18:30 config.json\n",
+      "-rw-r--r-- 1 root root 891091065 Aug  7 18:30 model.onnx\n",
+      "-rw-r--r-- 1 root root       286 Aug  7 18:30 special_tokens_map.json\n",
+      "-rw-r--r-- 1 root root      1246 Aug  7 18:30 tokenizer_config.json\n",
+      "-rw-r--r-- 1 root root   2272346 Aug  7 18:30 tokenizer.json\n",
+      "\n",
+      "onnx_models/DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT/assets:\n",
+      "total 748\n",
+      "-rw-r--r-- 1 root root     21 Aug  7 18:30 labels.txt\n",
+      "-rw-r--r-- 1 root root 760289 Aug  7 18:30 spiece.model\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lR {ONNX_MODEL}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "RJTACAHVD_53",
+    "outputId": "63806f1f-81dd-4785-cc0f-4cc57254ea73"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entailment\n",
+      "contradict"
+     ]
+    }
+   ],
+   "source": [
+    "!cat {ONNX_MODEL}/assets/labels.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "MtZFJ3geOXM2"
+   },
+   "source": [
+    "## Import and Save AlbertForZeroShotClassification in Spark NLP\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tKGcJKB_OXM2"
+   },
+   "source": [
+    "- Let's install and setup Spark NLP in Google Colab\n",
+    "- This part is pretty easy via our simple script"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "r2-9OsKzOXM2"
+   },
+   "outputs": [],
+   "source": [
+    "! wget http://setup.johnsnowlabs.com/colab.sh -O - | bash"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uQrimXpqOXM2"
+   },
+   "source": [
+    "Let's start Spark with Spark NLP included via our simple `start()` function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Kl2EKK-lOXM3",
+    "outputId": "642c6948-78b0-4f4d-d11b-1b2b57aeaf3c"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/lib/python3.10/subprocess.py:1796: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
+      "  self.pid = _posixsubprocess.fork_exec(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Apache Spark version: 3.4.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sparknlp\n",
+    "# let's start Spark with Spark NLP\n",
+    "spark = sparknlp.start()\n",
+    "\n",
+    "print(\"Apache Spark version: {}\".format(spark.version))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cIoqa8jpOXM3"
+   },
+   "source": [
+    "- Let's use `loadSavedModel` functon in `AlbertForZeroShotClassification` which allows us to load ONNX model in SavedModel format\n",
+    "- Most params can be set later when you are loading this model in `AlbertForZeroShotClassification` in runtime like `setMaxSentenceLength`, so don't worry what you are setting them now\n",
+    "- `loadSavedModel` accepts two params, first is the path to the TF SavedModel. The second is the SparkSession that is `spark` variable we previously started via `sparknlp.start()`\n",
+    "- NOTE: `loadSavedModel` accepts local paths in addition to distributed file systems such as `HDFS`, `S3`, `DBFS`, etc. This feature was introduced in Spark NLP 4.2.2 release. Keep in mind the best and recommended way to move/share/reuse Spark NLP models is to use `write.save` so you can use `.load()` from any file systems natively."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "U--84qnxOXM3"
+   },
+   "source": [
+    "- Let's save it on disk so it is easier to be moved around and also be used later via `.load` function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "id": "tdU3Olvg-o6m"
+   },
+   "outputs": [],
+   "source": [
+    "from sparknlp.annotator import *\n",
+    "\n",
+    "zero_shot_classifier = AlbertForZeroShotClassification\\\n",
+    "  .loadSavedModel(ONNX_MODEL, spark)\\\n",
+    "  .setInputCols([\"document\",'token'])\\\n",
+    "  .setOutputCol(\"class\")\\\n",
+    "  .setCaseSensitive(False)\\\n",
+    "  .setMaxSentenceLength(128)\\\n",
+    "  .setCandidateLabels([\"urgent\", \"mobile\", \"technology\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "id": "FeL-PyNLOXM3"
+   },
+   "outputs": [],
+   "source": [
+    "zero_shot_classifier.write().overwrite().save(\"./{}_spark_nlp_onnx\".format(ONNX_MODEL))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KiX_wtJzOXM3"
+   },
+   "source": [
+    "Let's clean up stuff we don't need anymore"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "id": "-QDg7Y6bOXM3"
+   },
+   "outputs": [],
+   "source": [
+    "!rm -rf {ONNX_MODEL}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Bfk3hT24OXM3"
+   },
+   "source": [
+    "Awesome 😎  !\n",
+    "\n",
+    "This is your AlbertForZeroShotClassification model from HuggingFace 🤗  loaded and saved by Spark NLP 🚀"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "id": "IEbYe8JfOXM4"
+   },
+   "outputs": [],
+   "source": [
+    "from sparknlp.annotator import *\n",
+    "\n",
+    "zero_shot_classifier_loaded = AlbertForZeroShotClassification.load(\"./{}_spark_nlp_onnx\".format(ONNX_MODEL))\\\n",
+    "  .setInputCols([\"document\",'token'])\\\n",
+    "  .setOutputCol(\"multi_class\") \\\n",
+    "  .setCandidateLabels([\"urgent\", \"mobile\", \"technology\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "BQgl3FIvOXM4",
+    "outputId": "0f057b4b-35fd-43aa-fb5e-7e89f1fa58b2"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 871096\n",
+      "-rw-r--r-- 1 root root 891227169 Aug  7 18:31 albert_classification_onnx\n",
+      "-rw-r--r-- 1 root root    760289 Aug  7 18:31 albert_spp\n",
+      "drwxr-xr-x 3 root root      4096 Aug  7 18:31 fields\n",
+      "drwxr-xr-x 2 root root      4096 Aug  7 18:31 metadata\n"
+     ]
+    }
+   ],
+   "source": [
+    "! ls -l {ONNX_MODEL}_spark_nlp_onnx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Fz3MtMcUOXM4"
+   },
+   "source": [
+    "Now let's see how we can use it on other machines, clusters, or any place you wish to use your new and shiny AlbertForZeroShotClassification model 😊"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "i9DBBy2QOXM4"
+   },
+   "source": [
+    "This is how you can use your loaded classifier model in Spark NLP 🚀 pipeline:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pojxPjIfOXM4",
+    "outputId": "ec82cd67-0ea2-4146-b1b5-a533ddd0ad6c"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+--------+\n",
+      "|                text|  result|\n",
+      "+--------------------+--------+\n",
+      "|I have a problem ...|[urgent]|\n",
+      "+--------------------+--------+\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pyspark.ml import Pipeline\n",
+    "\n",
+    "from sparknlp.base import *\n",
+    "from sparknlp.annotator import *\n",
+    "\n",
+    "document_assembler = DocumentAssembler() \\\n",
+    "    .setInputCol('text') \\\n",
+    "    .setOutputCol('document')\n",
+    "\n",
+    "tokenizer = Tokenizer() \\\n",
+    "    .setInputCols(['document']) \\\n",
+    "    .setOutputCol('token')\n",
+    "\n",
+    "pipeline = Pipeline(stages=[\n",
+    "    document_assembler,\n",
+    "    tokenizer,\n",
+    "    zero_shot_classifier_loaded\n",
+    "])\n",
+    "\n",
+    "# couple of simple examples\n",
+    "example = spark.createDataFrame([[\"I have a problem with my iphone that needs to be resolved asap!!\"]]).toDF(\"text\")\n",
+    "\n",
+    "result = pipeline.fit(example).transform(example)\n",
+    "\n",
+    "# result is a DataFrame\n",
+    "result.select(\"text\", \"multi_class.result\").show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "MlzWRhQyOXM5"
+   },
+   "source": [
+    "That's it! You can now go wild and use hundreds of `AlbertForZeroShotClassification` models from HuggingFace 🤗 in Spark NLP 🚀\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "L4",
+   "machine_shape": "hm",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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diff --git a/python/sparknlp/annotator/classifier_dl/__init__.py b/python/sparknlp/annotator/classifier_dl/__init__.py
index 2ecbe83018301c..ed3e7160892349 100644
--- a/python/sparknlp/annotator/classifier_dl/__init__.py
+++ b/python/sparknlp/annotator/classifier_dl/__init__.py
@@ -51,4 +51,5 @@
 from sparknlp.annotator.classifier_dl.deberta_for_zero_shot_classification import *
 from sparknlp.annotator.classifier_dl.mpnet_for_sequence_classification import *
 from sparknlp.annotator.classifier_dl.mpnet_for_question_answering import *
-from sparknlp.annotator.classifier_dl.mpnet_for_token_classification import *
\ No newline at end of file
+from sparknlp.annotator.classifier_dl.mpnet_for_token_classification import *
+from sparknlp.annotator.classifier_dl.albert_for_zero_shot_classification import *
\ No newline at end of file
diff --git a/python/sparknlp/annotator/classifier_dl/albert_for_zero_shot_classification.py b/python/sparknlp/annotator/classifier_dl/albert_for_zero_shot_classification.py
new file mode 100644
index 00000000000000..4ea47dab83166e
--- /dev/null
+++ b/python/sparknlp/annotator/classifier_dl/albert_for_zero_shot_classification.py
@@ -0,0 +1,211 @@
+#  Copyright 2017-2024 John Snow Labs
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+"""Contains classes for AlbertForZeroShotClassification."""
+
+from sparknlp.common import *
+
+
+class AlbertForZeroShotClassification(AnnotatorModel,
+                                      HasCaseSensitiveProperties,
+                                      HasBatchedAnnotate,
+                                      HasClassifierActivationProperties,
+                                      HasCandidateLabelsProperties,
+                                      HasEngine,
+                                      HasMaxSentenceLengthLimit):
+    """AlbertForZeroShotClassification using a `ModelForSequenceClassification` trained on NLI (natural language
+    inference) tasks. Equivalent of `DistilBertForSequenceClassification` models, but these models don't require a hardcoded
+    number of potential classes, they can be chosen at runtime. It usually means it's slower but it is much more
+    flexible.
+
+    Note that the model will loop through all provided labels. So the more labels you have, the
+    longer this process will take.
+
+    Any combination of sequences and labels can be passed and each combination will be posed as a premise/hypothesis
+    pair and passed to the pretrained model.
+
+    Pretrained models can be loaded with :meth:`.pretrained` of the companion
+    object:
+
+    >>> sequenceClassifier = AlbertForZeroShotClassification.pretrained() \\
+    ...     .setInputCols(["token", "document"]) \\
+    ...     .setOutputCol("label")
+
+    The default model is ``"albert_base_zero_shot_classifier_onnx"``, if no name is
+    provided.
+
+    For available pretrained models please see the `Models Hub
+    <https://sparknlp.orgtask=Text+Classification>`__.
+
+    To see which models are compatible and how to import them see
+    `Import Transformers into Spark NLP 🚀
+    <https://github.com/JohnSnowLabs/spark-nlp/discussions/5669>`_.
+
+    ====================== ======================
+    Input Annotation types Output Annotation type
+    ====================== ======================
+    ``DOCUMENT, TOKEN``    ``CATEGORY``
+    ====================== ======================
+
+    Parameters
+    ----------
+    batchSize
+        Batch size. Large values allows faster processing but requires more
+        memory, by default 8
+    caseSensitive
+        Whether to ignore case in tokens for embeddings matching, by default
+        True
+    configProtoBytes
+        ConfigProto from tensorflow, serialized into byte array.
+    maxSentenceLength
+        Max sentence length to process, by default 128
+    coalesceSentences
+        Instead of 1 class per sentence (if inputCols is `sentence`) output 1
+        class per document by averaging probabilities in all sentences, by
+        default False
+    activation
+        Whether to calculate logits via Softmax or Sigmoid, by default
+        `"softmax"`.
+
+    Examples
+    --------
+    >>> import sparknlp
+    >>> from sparknlp.base import *
+    >>> from sparknlp.annotator import *
+    >>> from pyspark.ml import Pipeline
+    >>> documentAssembler = DocumentAssembler() \\
+    ...     .setInputCol("text") \\
+    ...     .setOutputCol("document")
+    >>> tokenizer = Tokenizer() \\
+    ...     .setInputCols(["document"]) \\
+    ...     .setOutputCol("token")
+    >>> sequenceClassifier = AlbertForZeroShotClassification.pretrained() \\
+    ...     .setInputCols(["token", "document"]) \\
+    ...     .setOutputCol("label") \\
+    ...     .setCaseSensitive(True)
+    >>> pipeline = Pipeline().setStages([
+    ...     documentAssembler,
+    ...     tokenizer,
+    ...     sequenceClassifier
+    ... ])
+    >>> data = spark.createDataFrame([["I have a problem with my iphone that needs to be resolved asap!!"]]).toDF("text")
+    >>> result = pipeline.fit(data).transform(data)
+    >>> result.select("label.result").show(truncate=False)
+    +---------+
+    |result   |
+    +---------+
+    |[urgent] |
+    +---------+
+    """
+    name = "AlbertForZeroShotClassification"
+
+    inputAnnotatorTypes = [AnnotatorType.DOCUMENT, AnnotatorType.TOKEN]
+
+    outputAnnotatorType = AnnotatorType.CATEGORY
+
+    configProtoBytes = Param(Params._dummy(),
+                             "configProtoBytes",
+                             "ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()",
+                             TypeConverters.toListInt)
+
+    coalesceSentences = Param(Params._dummy(), "coalesceSentences",
+                              "Instead of 1 class per sentence (if inputCols is '''sentence''') output 1 class per document by averaging probabilities in all sentences.",
+                              TypeConverters.toBoolean)
+
+    def getClasses(self):
+        """
+        Returns labels used to train this model
+        """
+        return self._call_java("getClasses")
+
+    def setConfigProtoBytes(self, b):
+        """Sets configProto from tensorflow, serialized into byte array.
+
+        Parameters
+        ----------
+        b : List[int]
+            ConfigProto from tensorflow, serialized into byte array
+        """
+        return self._set(configProtoBytes=b)
+
+    def setCoalesceSentences(self, value):
+        """Instead of 1 class per sentence (if inputCols is '''sentence''') output 1 class per document by averaging
+        probabilities in all sentences. Due to max sequence length limit in almost all transformer models such as Bart
+        (512 tokens), this parameter helps to feed all the sentences into the model and averaging all the probabilities
+        for the entire document instead of probabilities per sentence. (Default: true)
+
+        Parameters
+        ----------
+        value : bool
+            If the output of all sentences will be averaged to one output
+        """
+        return self._set(coalesceSentences=value)
+
+    @keyword_only
+    def __init__(self, classname="com.johnsnowlabs.nlp.annotators.classifier.dl.AlbertForZeroShotClassification",
+                 java_model=None):
+        super(AlbertForZeroShotClassification, self).__init__(
+            classname=classname,
+            java_model=java_model
+        )
+        self._setDefault(
+            batchSize=8,
+            maxSentenceLength=128,
+            caseSensitive=True,
+            coalesceSentences=False,
+            activation="softmax"
+        )
+
+    @staticmethod
+    def loadSavedModel(folder, spark_session):
+        """Loads a locally saved model.
+
+        Parameters
+        ----------
+        folder : str
+            Folder of the saved model
+            spark_session : pyspark.sql.SparkSession
+            The current SparkSession
+
+        Returns
+        -------
+        AlbertForZeroShotClassification
+            The restored model
+        """
+        from sparknlp.internal import _AlbertForZeroShotClassificationLoader
+        jModel = _AlbertForZeroShotClassificationLoader(folder, spark_session._jsparkSession)._java_obj
+        return AlbertForZeroShotClassification(java_model=jModel)
+
+    @staticmethod
+    def pretrained(name="albert_zero_shot_classifier_onnx", lang="en", remote_loc=None):
+        """Downloads and loads a pretrained model.
+
+        Parameters
+        ----------
+        name : str, optional
+            Name of the pretrained model, by default
+            "albert_zero_shot_classifier_onnx"
+            lang : str, optional
+            Language of the pretrained model, by default "en"
+            remote_loc : str, optional
+            Optional remote address of the resource, by default None. Will use
+            Spark NLPs repositories otherwise.
+
+        Returns
+        -------
+        BartForZeroShotClassification
+            The restored model
+        """
+        from sparknlp.pretrained import ResourceDownloader
+        return ResourceDownloader.downloadModel(AlbertForZeroShotClassification, name, lang, remote_loc)
\ No newline at end of file
diff --git a/python/sparknlp/internal/__init__.py b/python/sparknlp/internal/__init__.py
index dc53a009506319..50588a08fe09de 100644
--- a/python/sparknlp/internal/__init__.py
+++ b/python/sparknlp/internal/__init__.py
@@ -58,6 +58,15 @@ def __init__(self, path, jspark):
         )
 
 
+class _AlbertForZeroShotClassificationLoader(ExtendedJavaWrapper):
+    def __init__(self, path, jspark):
+        super(_AlbertForZeroShotClassificationLoader, self).__init__(
+            "com.johnsnowlabs.nlp.annotators.classifier.dl.AlbertForZeroShotClassification.loadSavedModel",
+            path,
+            jspark,
+        )
+
+
 class _BertLoader(ExtendedJavaWrapper):
     def __init__(self, path, jspark, use_openvino=False):
         super(_BertLoader, self).__init__(
diff --git a/python/test/annotator/classifier_dl/albert_for_zero_shot_classification_test.py b/python/test/annotator/classifier_dl/albert_for_zero_shot_classification_test.py
new file mode 100644
index 00000000000000..7afda4131c4470
--- /dev/null
+++ b/python/test/annotator/classifier_dl/albert_for_zero_shot_classification_test.py
@@ -0,0 +1,60 @@
+#  Copyright 2017-2024 John Snow Labs
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+import unittest
+
+import pytest
+
+from sparknlp.annotator import *
+from sparknlp.base import *
+from test.annotator.common.has_max_sentence_length_test import HasMaxSentenceLengthTests
+from test.util import SparkContextForTest
+
+
+@pytest.mark.slow
+class AlbertForZeroShotClassificationTestSpec(unittest.TestCase, HasMaxSentenceLengthTests):
+    def setUp(self):
+        self.text = "I have a problem with my iphone that needs to be resolved asap!!"
+        self.data = SparkContextForTest.spark \
+            .createDataFrame([[self.text]]).toDF("text")
+        self.candidate_labels = ["urgent", "mobile", "technology"]
+
+        self.tested_annotator = AlbertForZeroShotClassification \
+            .pretrained()\
+            .setInputCols(["document", "token"]) \
+            .setOutputCol("multi_class") \
+            .setCandidateLabels(self.candidate_labels)
+
+    def test_run(self):
+        document_assembler = DocumentAssembler() \
+            .setInputCol("text") \
+            .setOutputCol("document")
+
+        tokenizer = Tokenizer().setInputCols("document").setOutputCol("token")
+
+        doc_classifier = self.tested_annotator
+
+        pipeline = Pipeline(stages=[
+            document_assembler,
+            tokenizer,
+            doc_classifier
+        ])
+
+        model = pipeline.fit(self.data)
+        model.transform(self.data).show()
+
+        light_pipeline = LightPipeline(model)
+        annotations_result = light_pipeline.fullAnnotate(self.text)
+        multi_class_result = annotations_result[0]["multi_class"][0].result
+        self.assertIn(multi_class_result, self.candidate_labels)
diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/AlbertClassification.scala b/src/main/scala/com/johnsnowlabs/ml/ai/AlbertClassification.scala
index 748f0d4f81e771..206d6175d12206 100644
--- a/src/main/scala/com/johnsnowlabs/ml/ai/AlbertClassification.scala
+++ b/src/main/scala/com/johnsnowlabs/ml/ai/AlbertClassification.scala
@@ -25,10 +25,12 @@ import com.johnsnowlabs.ml.tensorflow.sign.{ModelSignatureConstants, ModelSignat
 import com.johnsnowlabs.ml.tensorflow.{TensorResources, TensorflowWrapper}
 import com.johnsnowlabs.ml.util.{ONNX, Openvino, TensorFlow}
 import com.johnsnowlabs.nlp.annotators.common._
+import com.johnsnowlabs.nlp.annotators.tokenizer.wordpiece.BasicTokenizer
 import com.johnsnowlabs.nlp.{ActivationFunction, Annotation}
 import org.intel.openvino.Tensor
 import org.tensorflow.ndarray.buffer.IntDataBuffer
 import org.slf4j.{Logger, LoggerFactory}
+import org.tensorflow.ndarray.buffer.IntDataBuffer
 
 import scala.collection.JavaConverters._
 
@@ -95,7 +97,19 @@ private[johnsnowlabs] class AlbertClassification(
   def tokenizeSeqString(
       candidateLabels: Seq[String],
       maxSeqLength: Int,
-      caseSensitive: Boolean): Seq[WordpieceTokenizedSentence] = ???
+      caseSensitive: Boolean): Seq[WordpieceTokenizedSentence] = {
+    val basicTokenizer = new BasicTokenizer(caseSensitive)
+    val encoder =
+      new SentencepieceEncoder(spp, caseSensitive, sentencePieceDelimiterId, pieceIdOffset = 1)
+
+    val labelsToSentences = candidateLabels.map { s => Sentence(s, 0, s.length - 1, 0) }
+
+    labelsToSentences.map(label => {
+      val tokens = basicTokenizer.tokenize(label)
+      val wordpieceTokens = tokens.flatMap(token => encoder.encode(token)).take(maxSeqLength)
+      WordpieceTokenizedSentence(wordpieceTokens)
+    })
+  }
 
   def tokenizeDocument(
       docs: Seq[Annotation],
@@ -310,7 +324,30 @@ private[johnsnowlabs] class AlbertClassification(
       batch: Seq[Array[Int]],
       entailmentId: Int,
       contradictionId: Int,
-      activation: String): Array[Array[Float]] = ???
+      activation: String): Array[Array[Float]] = {
+
+    val maxSentenceLength = batch.map(encodedSentence => encodedSentence.length).max
+    val paddedBatch = batch.map(arr => padArrayWithZeros(arr, maxSentenceLength))
+    val batchLength = paddedBatch.length
+
+    val rawScores = detectedEngine match {
+      case TensorFlow.name => getRawScoresWithTF(paddedBatch, maxSentenceLength)
+      case ONNX.name => getRawScoresWithOnnx(paddedBatch, maxSentenceLength, sequence = true)
+    }
+
+    val dim = rawScores.length / batchLength
+    rawScores
+      .grouped(dim)
+      .toArray
+  }
+
+  private def padArrayWithZeros(arr: Array[Int], maxLength: Int): Array[Int] = {
+    if (arr.length >= maxLength) {
+      arr
+    } else {
+      arr ++ Array.fill(maxLength - arr.length)(0)
+    }
+  }
 
   def tagSpan(batch: Seq[Array[Int]]): (Array[Array[Float]], Array[Array[Float]]) = {
     val batchLength = batch.length
diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotator.scala b/src/main/scala/com/johnsnowlabs/nlp/annotator.scala
index 5d8197ba018e21..98387ad04fb14a 100644
--- a/src/main/scala/com/johnsnowlabs/nlp/annotator.scala
+++ b/src/main/scala/com/johnsnowlabs/nlp/annotator.scala
@@ -432,6 +432,13 @@ package object annotator {
       extends ReadablePretrainedAlbertForTokenModel
       with ReadAlbertForTokenDLModel
 
+  type AlbertForZeroShotClassification =
+    com.johnsnowlabs.nlp.annotators.classifier.dl.AlbertForZeroShotClassification
+
+  object AlbertForZeroShotClassification
+      extends ReadablePretrainedAlbertForZeroShotModel
+      with ReadAlbertForZeroShotDLModel
+
   type XlnetForTokenClassification =
     com.johnsnowlabs.nlp.annotators.classifier.dl.XlnetForTokenClassification
 
diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/AlbertForZeroShotClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/AlbertForZeroShotClassification.scala
new file mode 100644
index 00000000000000..6cb650237a604d
--- /dev/null
+++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/AlbertForZeroShotClassification.scala
@@ -0,0 +1,402 @@
+/*
+ * Copyright 2017-2024 John Snow Labs
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.johnsnowlabs.nlp.annotators.classifier.dl
+
+import com.johnsnowlabs.ml.ai.AlbertClassification
+import com.johnsnowlabs.ml.onnx.{OnnxWrapper, ReadOnnxModel, WriteOnnxModel}
+import com.johnsnowlabs.ml.tensorflow.sentencepiece.{
+  ReadSentencePieceModel,
+  SentencePieceWrapper,
+  WriteSentencePieceModel
+}
+import com.johnsnowlabs.ml.tensorflow.{
+  ReadTensorflowModel,
+  TensorflowWrapper,
+  WriteTensorflowModel
+}
+import com.johnsnowlabs.ml.util.LoadExternalModel.{
+  loadSentencePieceAsset,
+  loadTextAsset,
+  modelSanityCheck,
+  notSupportedEngineError
+}
+import com.johnsnowlabs.ml.util.{ONNX, TensorFlow}
+import com.johnsnowlabs.nlp._
+import com.johnsnowlabs.nlp.annotators.common.{SentenceSplit, TokenizedWithSentence}
+import com.johnsnowlabs.nlp.serialization.MapFeature
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.ml.param.{BooleanParam, IntArrayParam, IntParam}
+import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.sql.SparkSession
+
+class AlbertForZeroShotClassification(override val uid: String)
+    extends AnnotatorModel[AlbertForZeroShotClassification]
+    with HasBatchedAnnotate[AlbertForZeroShotClassification]
+    with WriteTensorflowModel
+    with WriteOnnxModel
+    with WriteSentencePieceModel
+    with HasCaseSensitiveProperties
+    with HasClassifierActivationProperties
+    with HasEngine
+    with HasCandidateLabelsProperties {
+
+  /** Annotator reference id. Used to identify elements in metadata or to refer to this annotator
+    * type
+    */
+  def this() = this(Identifiable.randomUID("ALBERT_FOR_ZERO_SHOT_CLASSIFICATION"))
+
+  /** Input Annotator Types: DOCUMENT, TOKEN
+    *
+    * @group anno
+    */
+  override val inputAnnotatorTypes: Array[AnnotatorType] =
+    Array(AnnotatorType.DOCUMENT, AnnotatorType.TOKEN)
+
+  /** Output Annotator Types: CATEGORY
+    *
+    * @group anno
+    */
+  override val outputAnnotatorType: AnnotatorType = AnnotatorType.CATEGORY
+
+  /** Labels used to decode predicted IDs back to string tags
+    *
+    * @group param
+    */
+  val labels: MapFeature[String, Int] = new MapFeature(this, "labels").setProtected()
+
+  /** @group setParam */
+  def setLabels(value: Map[String, Int]): this.type = {
+    if (get(labels).isEmpty)
+      set(labels, value)
+    this
+  }
+
+  /** Returns labels used to train this model */
+  def getClasses: Array[String] = {
+    $$(labels).keys.toArray
+  }
+
+  /** Instead of 1 class per sentence (if inputCols is '''sentence''') output 1 class per document
+    * by averaging probabilities in all sentences (Default: `false`).
+    *
+    * Due to max sequence length limit in almost all transformer models such as DeBerta (512
+    * tokens), this parameter helps feeding all the sentences into the model and averaging all the
+    * probabilities for the entire document instead of probabilities per sentence.
+    *
+    * @group param
+    */
+  val coalesceSentences = new BooleanParam(
+    this,
+    "coalesceSentences",
+    "If sets to true the output of  all sentences will be averaged to one output instead of one output per sentence. Defaults to false.")
+
+  /** @group setParam */
+  def setCoalesceSentences(value: Boolean): this.type = set(coalesceSentences, value)
+
+  /** @group getParam */
+  def getCoalesceSentences: Boolean = $(coalesceSentences)
+
+  /** ConfigProto from tensorflow, serialized into byte array. Get with
+    * `config_proto.SerializeToString()`
+    *
+    * @group param
+    */
+  val configProtoBytes = new IntArrayParam(
+    this,
+    "configProtoBytes",
+    "ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()")
+
+  /** @group setParam */
+  def setConfigProtoBytes(bytes: Array[Int]): AlbertForZeroShotClassification.this.type =
+    set(this.configProtoBytes, bytes)
+
+  /** @group getParam */
+  def getConfigProtoBytes: Option[Array[Byte]] = get(this.configProtoBytes).map(_.map(_.toByte))
+
+  /** Max sentence length to process (Default: `128`)
+    *
+    * @group param
+    */
+  val maxSentenceLength =
+    new IntParam(this, "maxSentenceLength", "Max sentence length to process")
+
+  /** @group setParam */
+  def setMaxSentenceLength(value: Int): this.type = {
+    require(
+      value <= 512,
+      "DeBerta models do not support sequences longer than 512 because of trainable positional embeddings.")
+    require(value >= 1, "The maxSentenceLength must be at least 1")
+    set(maxSentenceLength, value)
+    this
+  }
+
+  /** @group getParam */
+  def getMaxSentenceLength: Int = $(maxSentenceLength)
+
+  /** It contains TF model signatures for the laded saved model
+    *
+    * @group param
+    */
+  val signatures =
+    new MapFeature[String, String](model = this, name = "signatures").setProtected()
+
+  /** @group setParam */
+  def setSignatures(value: Map[String, String]): this.type = {
+    set(signatures, value)
+    this
+  }
+
+  /** @group getParam */
+  def getSignatures: Option[Map[String, String]] = get(this.signatures)
+
+  private var _model: Option[Broadcast[AlbertClassification]] = None
+
+  /** @group setParam */
+  def setModelIfNotSet(
+      spark: SparkSession,
+      tensorflowWrapper: Option[TensorflowWrapper],
+      onnxWrapper: Option[OnnxWrapper],
+      spp: SentencePieceWrapper): AlbertForZeroShotClassification = {
+    if (_model.isEmpty) {
+      _model = Some(
+        spark.sparkContext.broadcast(
+          new AlbertClassification(
+            tensorflowWrapper,
+            onnxWrapper,
+            spp,
+            configProtoBytes = getConfigProtoBytes,
+            tags = $$(labels),
+            signatures = getSignatures)))
+    }
+
+    this
+  }
+
+  /** @group getParam */
+  def getModelIfNotSet: AlbertClassification = _model.get.value
+
+  /** Whether to lowercase tokens or not (Default: `true`).
+    *
+    * @group setParam
+    */
+  override def setCaseSensitive(value: Boolean): this.type = {
+    set(this.caseSensitive, value)
+  }
+
+  setDefault(
+    batchSize -> 8,
+    maxSentenceLength -> 128,
+    caseSensitive -> true,
+    coalesceSentences -> false)
+
+  /** takes a document and annotations and produces new annotations of this annotator's annotation
+    * type
+    *
+    * @param batchedAnnotations
+    *   Annotations in batches that correspond to inputAnnotationCols generated by previous
+    *   annotators if any
+    * @return
+    *   any number of annotations processed for every batch of input annotations. Not necessary
+    *   one to one relationship
+    *
+    * IMPORTANT: !MUST! return sequences of equal lengths !! IMPORTANT: !MUST! return sentences
+    * that belong to the same original row !! (challenging)
+    */
+  override def batchAnnotate(batchedAnnotations: Seq[Array[Annotation]]): Seq[Seq[Annotation]] = {
+    batchedAnnotations.map(annotations => {
+      val sentences = SentenceSplit.unpack(annotations).toArray
+      val tokenizedSentences = TokenizedWithSentence.unpack(annotations).toArray
+
+      if (tokenizedSentences.nonEmpty) {
+        getModelIfNotSet.predictSequenceWithZeroShot(
+          tokenizedSentences,
+          sentences,
+          $(candidateLabels),
+          $(entailmentIdParam),
+          $(contradictionIdParam),
+          $(batchSize),
+          $(maxSentenceLength),
+          $(caseSensitive),
+          $(coalesceSentences),
+          $$(labels),
+          getActivation)
+
+      } else {
+        Seq.empty[Annotation]
+      }
+    })
+  }
+
+  override def onWrite(path: String, spark: SparkSession): Unit = {
+    super.onWrite(path, spark)
+    val suffix = "_albert_classification"
+
+    getEngine match {
+      case TensorFlow.name =>
+        writeTensorflowModelV2(
+          path,
+          spark,
+          getModelIfNotSet.tensorflowWrapper.get,
+          suffix,
+          AlbertForSequenceClassification.tfFile)
+      case ONNX.name =>
+        writeOnnxModel(
+          path,
+          spark,
+          getModelIfNotSet.onnxWrapper.get,
+          suffix,
+          AlbertForSequenceClassification.onnxFile)
+    }
+
+    writeSentencePieceModel(
+      path,
+      spark,
+      getModelIfNotSet.spp,
+      "_albert",
+      AlbertForSequenceClassification.sppFile)
+  }
+
+}
+
+trait ReadablePretrainedAlbertForZeroShotModel
+    extends ParamsAndFeaturesReadable[AlbertForZeroShotClassification]
+    with HasPretrained[AlbertForZeroShotClassification] {
+  override val defaultModelName: Some[String] = Some("albert_zero_shot_classifier_onnx")
+  override val defaultLang: String = "en"
+
+  /** Java compliant-overrides */
+  override def pretrained(): AlbertForZeroShotClassification = super.pretrained()
+
+  override def pretrained(name: String): AlbertForZeroShotClassification =
+    super.pretrained(name)
+
+  override def pretrained(name: String, lang: String): AlbertForZeroShotClassification =
+    super.pretrained(name, lang)
+
+  override def pretrained(
+      name: String,
+      lang: String,
+      remoteLoc: String): AlbertForZeroShotClassification =
+    super.pretrained(name, lang, remoteLoc)
+}
+
+trait ReadAlbertForZeroShotDLModel
+    extends ReadTensorflowModel
+    with ReadOnnxModel
+    with ReadSentencePieceModel {
+  this: ParamsAndFeaturesReadable[AlbertForZeroShotClassification] =>
+
+  override val tfFile: String = "albert_classification_tensorflow"
+  override val onnxFile: String = "albert_classification_onnx"
+  override val sppFile: String = "albert_spp"
+
+  def readModel(
+      instance: AlbertForZeroShotClassification,
+      path: String,
+      spark: SparkSession): Unit = {
+
+    val spp = readSentencePieceModel(path, spark, "_albert_spp", sppFile)
+
+    instance.getEngine match {
+      case TensorFlow.name =>
+        val tfWrapper = readTensorflowModel(path, spark, "_albert_classification_tf")
+        instance.setModelIfNotSet(spark, Some(tfWrapper), None, spp)
+      case ONNX.name =>
+        val onnxWrapper =
+          readOnnxModel(
+            path,
+            spark,
+            "albert_zero_classification_onnx",
+            zipped = true,
+            useBundle = false,
+            None)
+        instance.setModelIfNotSet(spark, None, Some(onnxWrapper), spp)
+      case _ =>
+        throw new Exception(notSupportedEngineError)
+    }
+  }
+
+  addReader(readModel)
+
+  def loadSavedModel(modelPath: String, spark: SparkSession): AlbertForZeroShotClassification = {
+
+    val (localModelPath, detectedEngine) = modelSanityCheck(modelPath)
+
+    val spModel = loadSentencePieceAsset(localModelPath, "spiece.model")
+    val labels = loadTextAsset(localModelPath, "labels.txt").zipWithIndex.toMap
+
+    val entailmentIds = labels.filter(x => x._1.toLowerCase().startsWith("entail")).values.toArray
+    val contradictionIds =
+      labels.filter(x => x._1.toLowerCase().startsWith("contradict")).values.toArray
+
+    require(
+      entailmentIds.length == 1 && contradictionIds.length == 1,
+      s"""This annotator supports classifiers trained on NLI datasets. You must have only at least 2 or maximum 3 labels in your dataset:
+
+          example with 3 labels: 'contradict', 'neutral', 'entailment'
+          example with 2 labels: 'contradict', 'entailment'
+
+          You can modify assets/labels.txt file to match the above format.
+
+          Current labels: ${labels.keys.mkString(", ")}
+          """)
+
+    val annotatorModel = new AlbertForZeroShotClassification()
+      .setLabels(labels)
+      .setCandidateLabels(labels.keys.toArray)
+
+    /* set the entailment id */
+    annotatorModel.set(annotatorModel.entailmentIdParam, entailmentIds.head)
+    /* set the contradiction id */
+    annotatorModel.set(annotatorModel.contradictionIdParam, contradictionIds.head)
+    /* set the engine */
+    annotatorModel.set(annotatorModel.engine, detectedEngine)
+
+    detectedEngine match {
+      case TensorFlow.name =>
+        val (wrapper, signatures) =
+          TensorflowWrapper.read(localModelPath, zipped = false, useBundle = true)
+
+        val _signatures = signatures match {
+          case Some(s) => s
+          case None => throw new Exception("Cannot load signature definitions from model!")
+        }
+
+        /** the order of setSignatures is important if we use getSignatures inside
+          * setModelIfNotSet
+          */
+        annotatorModel
+          .setSignatures(_signatures)
+          .setModelIfNotSet(spark, Some(wrapper), None, spModel)
+      case ONNX.name =>
+        val onnxWrapper =
+          OnnxWrapper.read(spark, localModelPath, zipped = false, useBundle = true)
+        annotatorModel.setModelIfNotSet(spark, None, Some(onnxWrapper), spModel)
+      case _ =>
+        throw new Exception(notSupportedEngineError)
+    }
+
+    annotatorModel
+  }
+}
+
+/** This is the companion object of [[AlbertForZeroShotClassification]]. Please refer to that
+  * class for the documentation.
+  */
+object AlbertForZeroShotClassification
+    extends ReadablePretrainedAlbertForZeroShotModel
+    with ReadAlbertForZeroShotDLModel
diff --git a/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala b/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala
index 467e565351c301..c4d887c3e03934 100644
--- a/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala
+++ b/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala
@@ -691,8 +691,9 @@ object PythonResourceDownloader {
     "LLAMA2Transformer" -> LLAMA2Transformer,
     "M2M100Transformer" -> M2M100Transformer,
     "UAEEmbeddings" -> UAEEmbeddings,
+    "AlbertForZeroShotClassification" -> AlbertForZeroShotClassification,
     "MxbaiEmbeddings" -> MxbaiEmbeddings,
-    "SnowFlakeEmbeddings" -> SnowFlakeEmbeddings
+    "SnowFlakeEmbeddings" -> SnowFlakeEmbeddings,
   )
 
   // List pairs of types such as the one with key type can load a pretrained model from the value type
diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/AlbertForZeroShotClassificationTestSpec.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/AlbertForZeroShotClassificationTestSpec.scala
new file mode 100644
index 00000000000000..06980c5e36eae2
--- /dev/null
+++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/AlbertForZeroShotClassificationTestSpec.scala
@@ -0,0 +1,66 @@
+/*
+ * Copyright 2017-2024 John Snow Labs
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.johnsnowlabs.nlp.annotators.classifier.dl
+
+import com.johnsnowlabs.nlp.annotators.Tokenizer
+import com.johnsnowlabs.nlp.base.DocumentAssembler
+import com.johnsnowlabs.nlp.util.io.ResourceHelper
+import com.johnsnowlabs.tags.SlowTest
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.sql.functions.explode
+import org.scalatest.flatspec.AnyFlatSpec
+
+class AlbertForZeroShotClassificationTestSpec extends AnyFlatSpec {
+
+  "AlbertForZeroShotClassification" should "correctly load custom ONNX model" taggedAs SlowTest in {
+    import ResourceHelper.spark.implicits._
+
+    val dataDf =
+      Seq("I have a problem with my iphone that needs to be resolved asap!!").toDF("text")
+
+    val document = new DocumentAssembler()
+      .setInputCol("text")
+      .setOutputCol("document")
+
+    val tokenizer = new Tokenizer()
+      .setInputCols(Array("document"))
+      .setOutputCol("token")
+
+    val zeroShotClassifier = AlbertForZeroShotClassification
+      .pretrained()
+      .setInputCols(Array("document", "token"))
+      .setOutputCol("multi_class")
+      .setCaseSensitive(true)
+      .setCoalesceSentences(true)
+      .setCandidateLabels(Array("urgent", "mobile", "technology"))
+
+    val pipeline = new Pipeline().setStages(Array(document, tokenizer, zeroShotClassifier))
+
+    val pipelineModel = pipeline.fit(dataDf)
+    val pipelineDF = pipelineModel.transform(dataDf)
+
+    pipelineDF.select("multi_class").show(false)
+    val totalDocs = pipelineDF.select(explode($"document.result")).count.toInt
+    val totalLabels = pipelineDF.select(explode($"multi_class.result")).count.toInt
+
+    println(s"total tokens: $totalDocs")
+    println(s"total labels: $totalLabels")
+
+    assert(totalDocs == totalLabels)
+  }
+
+}