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",
+      "f9bc1e1930094c9881d963e763886d46",
+      "f50399ac91fd4a578a4a03418de73f2c",
+      "8b0feec6f6fc443c9666de726e05511d",
+      "84437339b5fe40268e3d8b9403f8d7d7",
+      "a5cde1eb501b4082ae3ac1593fb9d867",
+      "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",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "0287e67435584efdb1fdcd50ca71e7f1": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_28b489e3099d4818bf4fda60decfc5ec",
+      "placeholder": "​",
+      "style": "IPY_MODEL_f20623e2768b41f18007e0e522abd51c",
+      "value": " 443M/443M [00:01&lt;00:00, 363MB/s]"
+     }
+    },
+    "0509da2e747b4a4abec55dc90ac7ea36": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HBoxModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HBoxModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HBoxView",
+      "box_style": "",
+      "children": [
+       "IPY_MODEL_9cb4064076734fb4b1f5dbdaf0628b93",
+       "IPY_MODEL_a18bc897f09b4cc184e9a2bb9ee596be",
+       "IPY_MODEL_34180f1b1a4e40068ad631787e16a963"
+      ],
+      "layout": "IPY_MODEL_b4e1ff7ab0ba49018ca4b7dcbffcb15f"
+     }
+    },
+    "0b5b53b7e07c4953809e70aa6460b48d": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "FloatProgressModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "FloatProgressModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "ProgressView",
+      "bar_style": "success",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_fb023dc778604d18b2ae71e6f134fb35",
+      "max": 929,
+      "min": 0,
+      "orientation": "horizontal",
+      "style": "IPY_MODEL_75584e408d5a45918592959d1166cd61",
+      "value": 929
+     }
+    },
+    "202252d3a8a34be0a0b7496a79289fea": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "207ab3bfc47e4c92a9a22eda29697a5d": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "2309b25a2978445d9cf83d596eb0b4e4": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "28b489e3099d4818bf4fda60decfc5ec": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "2f40598c047f45558c9ced5c1a53e1ce": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "30b687c0c6be4e31bba624e771a0d884": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "3195a3ef78654ee794622cf5cf8936ec": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "ProgressStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "ProgressStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "bar_color": null,
+      "description_width": ""
+     }
+    },
+    "34180f1b1a4e40068ad631787e16a963": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_ae2cb34043d741ddb1763b6f61fde965",
+      "placeholder": "​",
+      "style": "IPY_MODEL_ef525fceb9094a9ca34954a191a35651",
+      "value": " 508/508 [00:00&lt;00:00, 43.2kB/s]"
+     }
+    },
+    "3724e5d1406f4ba5b2cfa24ba79e294f": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "3f82e38f08f041929c62a312c8aeb009": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "FloatProgressModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "FloatProgressModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "ProgressView",
+      "bar_style": "success",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_202252d3a8a34be0a0b7496a79289fea",
+      "max": 1395301,
+      "min": 0,
+      "orientation": "horizontal",
+      "style": "IPY_MODEL_e2e7aded890546688347399ee044bc22",
+      "value": 1395301
+     }
+    },
+    "44f07151e3d14ff2afc48487c88a3de2": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "48fd2c7fee694cee86603eb4acf5da3f": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "4c3fd23c16c64a0bbfd383365c8c3f7e": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "547119d6ee0f482c9c28239749c0db49": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "583a35d6312c4154894d510740810eba": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "ProgressStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "ProgressStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "bar_color": null,
+      "description_width": ""
+     }
+    },
+    "5e78097f12b84b829d7e6c935839c131": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_30b687c0c6be4e31bba624e771a0d884",
+      "placeholder": "​",
+      "style": "IPY_MODEL_6b00c16ba3634a2388373e987abe2c2a",
+      "value": "model.safetensors: 100%"
+     }
+    },
+    "64d85c756c0c4d5699ea52e9121bc103": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "ProgressStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "ProgressStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "bar_color": null,
+      "description_width": ""
+     }
+    },
+    "65c1c148726a4e8085274716152036ae": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_aba3041fcb354473b272b1bc8b089240",
+      "placeholder": "​",
+      "style": "IPY_MODEL_888169b856834a10a9d2b63a4f2218ee",
+      "value": " 1.40M/1.40M [00:00&lt;00:00, 68.7MB/s]"
+     }
+    },
+    "696b354f894447f6975293fe2ea6a92b": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "FloatProgressModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "FloatProgressModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "ProgressView",
+      "bar_style": "success",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_8e57e27faa7f4aa59a72fffb8f07afb4",
+      "max": 810912,
+      "min": 0,
+      "orientation": "horizontal",
+      "style": "IPY_MODEL_be8f8e06494243879332788fa91aef56",
+      "value": 810912
+     }
+    },
+    "6b00c16ba3634a2388373e987abe2c2a": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "6cf9c6a811d74dacbceaad5705d5c71a": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "700fd5c2d7094788a65a16b928adbbfb": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "75584e408d5a45918592959d1166cd61": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "ProgressStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "ProgressStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "bar_color": null,
+      "description_width": ""
+     }
+    },
+    "78d1fc25a6494ab09c0782853b36aa6b": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "78f80709b196497486c9148e30446893": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "84437339b5fe40268e3d8b9403f8d7d7": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "850ad48422454088bb8ec39ab92dc783": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HBoxModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HBoxModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HBoxView",
+      "box_style": "",
+      "children": [
+       "IPY_MODEL_5e78097f12b84b829d7e6c935839c131",
+       "IPY_MODEL_94c128fadd7f4b9e95f336aad0a8d47d",
+       "IPY_MODEL_0287e67435584efdb1fdcd50ca71e7f1"
+      ],
+      "layout": "IPY_MODEL_adb519dd31f84449a24377400799661b"
+     }
+    },
+    "888169b856834a10a9d2b63a4f2218ee": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "89c1e714cb14442fb01379befe80c3b7": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_547119d6ee0f482c9c28239749c0db49",
+      "placeholder": "​",
+      "style": "IPY_MODEL_44f07151e3d14ff2afc48487c88a3de2",
+      "value": "config.json: 100%"
+     }
+    },
+    "8b0feec6f6fc443c9666de726e05511d": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_e2753fb7c8e4472b996c76314cb9e2df",
+      "placeholder": "​",
+      "style": "IPY_MODEL_2f40598c047f45558c9ced5c1a53e1ce",
+      "value": " 25.0/25.0 [00:00&lt;00:00, 2.09kB/s]"
+     }
+    },
+    "8e57e27faa7f4aa59a72fffb8f07afb4": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "8f87d812959a44428e254cfd464f8416": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_6cf9c6a811d74dacbceaad5705d5c71a",
+      "placeholder": "​",
+      "style": "IPY_MODEL_d68dd62aa2874a288586d5e9f1b05ab8",
+      "value": " 929/929 [00:00&lt;00:00, 74.3kB/s]"
+     }
+    },
+    "94c128fadd7f4b9e95f336aad0a8d47d": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "FloatProgressModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "FloatProgressModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "ProgressView",
+      "bar_style": "success",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_9eeb299678f248829a0eb28c9a912602",
+      "max": 442525380,
+      "min": 0,
+      "orientation": "horizontal",
+      "style": "IPY_MODEL_3195a3ef78654ee794622cf5cf8936ec",
+      "value": 442525380
+     }
+    },
+    "9cb4064076734fb4b1f5dbdaf0628b93": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_2309b25a2978445d9cf83d596eb0b4e4",
+      "placeholder": "​",
+      "style": "IPY_MODEL_b7802e819a1f494798429b61ed8215f9",
+      "value": "config.json: 100%"
+     }
+    },
+    "9eeb299678f248829a0eb28c9a912602": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "a056052820164a618cd026af7d2957b3": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "a18bc897f09b4cc184e9a2bb9ee596be": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "FloatProgressModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "FloatProgressModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "ProgressView",
+      "bar_style": "success",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_4c3fd23c16c64a0bbfd383365c8c3f7e",
+      "max": 508,
+      "min": 0,
+      "orientation": "horizontal",
+      "style": "IPY_MODEL_64d85c756c0c4d5699ea52e9121bc103",
+      "value": 508
+     }
+    },
+    "a5cde1eb501b4082ae3ac1593fb9d867": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "aba3041fcb354473b272b1bc8b089240": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "adb519dd31f84449a24377400799661b": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "ae2cb34043d741ddb1763b6f61fde965": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "b162fa1332084af2800088ae3dc9e46a": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "b4e1ff7ab0ba49018ca4b7dcbffcb15f": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "b7802e819a1f494798429b61ed8215f9": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "b910f989d1394268a21f25eb781cdcb6": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "bb13dda8b75b404f9b8981bf653fc4aa": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_78d1fc25a6494ab09c0782853b36aa6b",
+      "placeholder": "​",
+      "style": "IPY_MODEL_3724e5d1406f4ba5b2cfa24ba79e294f",
+      "value": "sentencepiece.bpe.model: 100%"
+     }
+    },
+    "be8f8e06494243879332788fa91aef56": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "ProgressStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "ProgressStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "bar_color": null,
+      "description_width": ""
+     }
+    },
+    "ce69f7e01dc1446287848f7996c1675a": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "d32f20584752439f87052117caa63142": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HBoxModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HBoxModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HBoxView",
+      "box_style": "",
+      "children": [
+       "IPY_MODEL_de3548618f4f402f82f153684f8942b9",
+       "IPY_MODEL_3f82e38f08f041929c62a312c8aeb009",
+       "IPY_MODEL_65c1c148726a4e8085274716152036ae"
+      ],
+      "layout": "IPY_MODEL_700fd5c2d7094788a65a16b928adbbfb"
+     }
+    },
+    "d3da72562dcc4e9c94358ba582c33a0c": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HBoxModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HBoxModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HBoxView",
+      "box_style": "",
+      "children": [
+       "IPY_MODEL_89c1e714cb14442fb01379befe80c3b7",
+       "IPY_MODEL_0b5b53b7e07c4953809e70aa6460b48d",
+       "IPY_MODEL_8f87d812959a44428e254cfd464f8416"
+      ],
+      "layout": "IPY_MODEL_207ab3bfc47e4c92a9a22eda29697a5d"
+     }
+    },
+    "d5676152d97940eab81e30e91ce7307e": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_b162fa1332084af2800088ae3dc9e46a",
+      "placeholder": "​",
+      "style": "IPY_MODEL_ecd52fa38cfc4a9fb2ba99f447deebec",
+      "value": " 811k/811k [00:00&lt;00:00, 1.92MB/s]"
+     }
+    },
+    "d68dd62aa2874a288586d5e9f1b05ab8": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "dbffadeee8fc4830b5249bb1f845cf46": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HBoxModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HBoxModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HBoxView",
+      "box_style": "",
+      "children": [
+       "IPY_MODEL_bb13dda8b75b404f9b8981bf653fc4aa",
+       "IPY_MODEL_696b354f894447f6975293fe2ea6a92b",
+       "IPY_MODEL_d5676152d97940eab81e30e91ce7307e"
+      ],
+      "layout": "IPY_MODEL_a056052820164a618cd026af7d2957b3"
+     }
+    },
+    "de3548618f4f402f82f153684f8942b9": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_b910f989d1394268a21f25eb781cdcb6",
+      "placeholder": "​",
+      "style": "IPY_MODEL_78f80709b196497486c9148e30446893",
+      "value": "tokenizer.json: 100%"
+     }
+    },
+    "e2753fb7c8e4472b996c76314cb9e2df": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "e2e7aded890546688347399ee044bc22": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "ProgressStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "ProgressStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "bar_color": null,
+      "description_width": ""
+     }
+    },
+    "ecd52fa38cfc4a9fb2ba99f447deebec": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "ed0b2575b2b04499973f13e1a7a0e586": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HBoxModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HBoxModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HBoxView",
+      "box_style": "",
+      "children": [
+       "IPY_MODEL_f9bc1e1930094c9881d963e763886d46",
+       "IPY_MODEL_f50399ac91fd4a578a4a03418de73f2c",
+       "IPY_MODEL_8b0feec6f6fc443c9666de726e05511d"
+      ],
+      "layout": "IPY_MODEL_84437339b5fe40268e3d8b9403f8d7d7"
+     }
+    },
+    "ef525fceb9094a9ca34954a191a35651": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "f20623e2768b41f18007e0e522abd51c": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "f50399ac91fd4a578a4a03418de73f2c": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "FloatProgressModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "FloatProgressModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "ProgressView",
+      "bar_style": "success",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_ce69f7e01dc1446287848f7996c1675a",
+      "max": 25,
+      "min": 0,
+      "orientation": "horizontal",
+      "style": "IPY_MODEL_583a35d6312c4154894d510740810eba",
+      "value": 25
+     }
+    },
+    "f9bc1e1930094c9881d963e763886d46": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_a5cde1eb501b4082ae3ac1593fb9d867",
+      "placeholder": "​",
+      "style": "IPY_MODEL_48fd2c7fee694cee86603eb4acf5da3f",
+      "value": "tokenizer_config.json: 100%"
+     }
+    },
+    "fb023dc778604d18b2ae71e6f134fb35": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
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",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m454.7/454.7 kB\u001b[0m \u001b[31m49.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\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": [
+      "eeeeff26c6d9466faa41b39c5a4fa669",
+      "df01abea998d42eaaa701105782abba0",
+      "b9d9b8a937964b85a66e8353c4d44f8d",
+      "9cbc3634d1d746b18c09b2ca6fe61a30",
+      "d2c6d0d6130e49f98360959d38f74d95",
+      "11b50210f33e4a8997b8c760d8e4ffe0",
+      "0ff93a6722634ec2b466371a2c968f9d",
+      "7657f8b6f33540d6b49aaf15bb73614a",
+      "f2ca64e852a94f9ba7dc4bf773a695ab",
+      "e1e015f4af6b47ec8b59807f35b2db07",
+      "5b2d19d102144e6f87b71be01c483b68",
+      "55c2a35733684494bdfa1d4fd053711c",
+      "3b2a3da584844cb3b83a193024c9ad40",
+      "d4a3ffb0fb924e8eb9eec47241abc317",
+      "6de7a0c6f13e4d8bb2c6af5d71be674e",
+      "b6402bb4b4884f79891914a56390b3e9",
+      "11f8cdf8c0c44daeab6303f50c7db818",
+      "acc61b054f424ec5848cea43e6a69672",
+      "337d30d86ffb4d068bc27a96b464d678",
+      "21f2c0ea2f7a441ca8fd88dd4a56f338",
+      "8be26127b62348dab6093cbd885a103a",
+      "99b4797f0808425b8dc0419ada9512fa",
+      "5e6f4369c79e4ff8aa5fa080d3658488",
+      "df594133572b4844a83e93d0c8db40c0",
+      "8f49629b99a84d5cb310c6530efc1012",
+      "49d0187f5da14cc99694bc08899e16a9",
+      "4b49e41be2b043a79d11aa73110b6258",
+      "a9c757b180fc4bfc93be39dd8d3f9cdd",
+      "3672f6662ce844c98f6d574ad37fbfaf",
+      "5f1c186f31d140aaa29e72299cdde746",
+      "504e7a1e464640f0beca9058a4011e1d",
+      "6a726bdd6135416e91637a034c5623fb",
+      "47a352883ad64b5ab3880c7dc23a1681",
+      "424a9faacc9b485aae5c472d68a55d66",
+      "c25961ebc54e48109438e724711c9b33",
+      "c7c6ed46459341caaebe21e339f2d6a7",
+      "fbb749170f894741a6eb818b19d576c3",
+      "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://raw.githubusercontent.com/JohnSnowLabs/spark-nlp/master/scripts/colab_setup.sh [following]\n",
+      "--2023-11-03 19:56:31--  https://raw.githubusercontent.com/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"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "08b3f87119504658b9a6deea86adce96": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "ProgressStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "ProgressStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "bar_color": null,
+      "description_width": ""
+     }
+    },
+    "099c6c294cfe49779b2e46932748cd7e": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "0ff93a6722634ec2b466371a2c968f9d": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "112531db46384f73af826f020f3df253": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "11593c36fc804d4db38f1b156c2d9456": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_bb494ef4a5f046d09a8b4cb52baac036",
+      "placeholder": "​",
+      "style": "IPY_MODEL_9f45321331ca42cd85ff84f0a7aa0fe8",
+      "value": "special_tokens_map.json: 100%"
+     }
+    },
+    "11b50210f33e4a8997b8c760d8e4ffe0": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "11f8cdf8c0c44daeab6303f50c7db818": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "21f2c0ea2f7a441ca8fd88dd4a56f338": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "ProgressStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "ProgressStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "bar_color": null,
+      "description_width": ""
+     }
+    },
+    "2269c361542a4ad6840513d5ba598ed5": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "FloatProgressModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "FloatProgressModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "ProgressView",
+      "bar_style": "success",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_9412a08772a941acabd75ded6573120d",
+      "max": 354,
+      "min": 0,
+      "orientation": "horizontal",
+      "style": "IPY_MODEL_b381561928f44dce9da1d692ae01e9f1",
+      "value": 354
+     }
+    },
+    "2ea468d131be464a8a74f9ec88f9eb4c": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "337d30d86ffb4d068bc27a96b464d678": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "3672f6662ce844c98f6d574ad37fbfaf": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "3b2a3da584844cb3b83a193024c9ad40": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_11f8cdf8c0c44daeab6303f50c7db818",
+      "placeholder": "​",
+      "style": "IPY_MODEL_acc61b054f424ec5848cea43e6a69672",
+      "value": "model.safetensors: 100%"
+     }
+    },
+    "40dc70033f81402db224255a72a90ede": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "424a9faacc9b485aae5c472d68a55d66": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HBoxModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HBoxModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HBoxView",
+      "box_style": "",
+      "children": [
+       "IPY_MODEL_c25961ebc54e48109438e724711c9b33",
+       "IPY_MODEL_c7c6ed46459341caaebe21e339f2d6a7",
+       "IPY_MODEL_fbb749170f894741a6eb818b19d576c3"
+      ],
+      "layout": "IPY_MODEL_112531db46384f73af826f020f3df253"
+     }
+    },
+    "47a352883ad64b5ab3880c7dc23a1681": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "49d0187f5da14cc99694bc08899e16a9": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_6a726bdd6135416e91637a034c5623fb",
+      "placeholder": "​",
+      "style": "IPY_MODEL_47a352883ad64b5ab3880c7dc23a1681",
+      "value": " 516/516 [00:00&lt;00:00, 44.6kB/s]"
+     }
+    },
+    "4b49e41be2b043a79d11aa73110b6258": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "504e7a1e464640f0beca9058a4011e1d": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "ProgressStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "ProgressStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "bar_color": null,
+      "description_width": ""
+     }
+    },
+    "54bd32b592f842d6ab4cbafe7486714b": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_2ea468d131be464a8a74f9ec88f9eb4c",
+      "placeholder": "​",
+      "style": "IPY_MODEL_8b79db60b5494b54964acb5048532d34",
+      "value": " 354/354 [00:00&lt;00:00, 28.4kB/s]"
+     }
+    },
+    "55c2a35733684494bdfa1d4fd053711c": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HBoxModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HBoxModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HBoxView",
+      "box_style": "",
+      "children": [
+       "IPY_MODEL_3b2a3da584844cb3b83a193024c9ad40",
+       "IPY_MODEL_d4a3ffb0fb924e8eb9eec47241abc317",
+       "IPY_MODEL_6de7a0c6f13e4d8bb2c6af5d71be674e"
+      ],
+      "layout": "IPY_MODEL_b6402bb4b4884f79891914a56390b3e9"
+     }
+    },
+    "5b2d19d102144e6f87b71be01c483b68": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "5e6f4369c79e4ff8aa5fa080d3658488": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HBoxModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HBoxModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HBoxView",
+      "box_style": "",
+      "children": [
+       "IPY_MODEL_df594133572b4844a83e93d0c8db40c0",
+       "IPY_MODEL_8f49629b99a84d5cb310c6530efc1012",
+       "IPY_MODEL_49d0187f5da14cc99694bc08899e16a9"
+      ],
+      "layout": "IPY_MODEL_4b49e41be2b043a79d11aa73110b6258"
+     }
+    },
+    "5f1c186f31d140aaa29e72299cdde746": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "6a726bdd6135416e91637a034c5623fb": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "6de7a0c6f13e4d8bb2c6af5d71be674e": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_8be26127b62348dab6093cbd885a103a",
+      "placeholder": "​",
+      "style": "IPY_MODEL_99b4797f0808425b8dc0419ada9512fa",
+      "value": " 443M/443M [00:27&lt;00:00, 14.7MB/s]"
+     }
+    },
+    "7657f8b6f33540d6b49aaf15bb73614a": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "7a04e2480edf4b9b81322c45c0210e55": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "8112a8a0c34a474bb0229c91b0ae88e0": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "8b79db60b5494b54964acb5048532d34": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "8be26127b62348dab6093cbd885a103a": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "8f49629b99a84d5cb310c6530efc1012": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "FloatProgressModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "FloatProgressModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "ProgressView",
+      "bar_style": "success",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_5f1c186f31d140aaa29e72299cdde746",
+      "max": 516,
+      "min": 0,
+      "orientation": "horizontal",
+      "style": "IPY_MODEL_504e7a1e464640f0beca9058a4011e1d",
+      "value": 516
+     }
+    },
+    "9173d5126c794468a40f83a2744808d6": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "9412a08772a941acabd75ded6573120d": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "99b4797f0808425b8dc0419ada9512fa": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "9cbc3634d1d746b18c09b2ca6fe61a30": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_e1e015f4af6b47ec8b59807f35b2db07",
+      "placeholder": "​",
+      "style": "IPY_MODEL_5b2d19d102144e6f87b71be01c483b68",
+      "value": " 929/929 [00:00&lt;00:00, 81.4kB/s]"
+     }
+    },
+    "9f45321331ca42cd85ff84f0a7aa0fe8": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "a9c757b180fc4bfc93be39dd8d3f9cdd": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "acc61b054f424ec5848cea43e6a69672": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "b381561928f44dce9da1d692ae01e9f1": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "ProgressStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "ProgressStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "bar_color": null,
+      "description_width": ""
+     }
+    },
+    "b6402bb4b4884f79891914a56390b3e9": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "b9d9b8a937964b85a66e8353c4d44f8d": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "FloatProgressModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "FloatProgressModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "ProgressView",
+      "bar_style": "success",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_7657f8b6f33540d6b49aaf15bb73614a",
+      "max": 929,
+      "min": 0,
+      "orientation": "horizontal",
+      "style": "IPY_MODEL_f2ca64e852a94f9ba7dc4bf773a695ab",
+      "value": 929
+     }
+    },
+    "bb494ef4a5f046d09a8b4cb52baac036": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "c25961ebc54e48109438e724711c9b33": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_7a04e2480edf4b9b81322c45c0210e55",
+      "placeholder": "​",
+      "style": "IPY_MODEL_9173d5126c794468a40f83a2744808d6",
+      "value": "tokenizer.json: 100%"
+     }
+    },
+    "c7c6ed46459341caaebe21e339f2d6a7": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "FloatProgressModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "FloatProgressModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "ProgressView",
+      "bar_style": "success",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_dff293d56f7b484fb54f0c7d2ddd1643",
+      "max": 2420898,
+      "min": 0,
+      "orientation": "horizontal",
+      "style": "IPY_MODEL_08b3f87119504658b9a6deea86adce96",
+      "value": 2420898
+     }
+    },
+    "d2c6d0d6130e49f98360959d38f74d95": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "d4a3ffb0fb924e8eb9eec47241abc317": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "FloatProgressModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "FloatProgressModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "ProgressView",
+      "bar_style": "success",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_337d30d86ffb4d068bc27a96b464d678",
+      "max": 442525380,
+      "min": 0,
+      "orientation": "horizontal",
+      "style": "IPY_MODEL_21f2c0ea2f7a441ca8fd88dd4a56f338",
+      "value": 442525380
+     }
+    },
+    "df01abea998d42eaaa701105782abba0": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_11b50210f33e4a8997b8c760d8e4ffe0",
+      "placeholder": "​",
+      "style": "IPY_MODEL_0ff93a6722634ec2b466371a2c968f9d",
+      "value": "config.json: 100%"
+     }
+    },
+    "df594133572b4844a83e93d0c8db40c0": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_a9c757b180fc4bfc93be39dd8d3f9cdd",
+      "placeholder": "​",
+      "style": "IPY_MODEL_3672f6662ce844c98f6d574ad37fbfaf",
+      "value": "tokenizer_config.json: 100%"
+     }
+    },
+    "dff293d56f7b484fb54f0c7d2ddd1643": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "e1e015f4af6b47ec8b59807f35b2db07": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "eeeeff26c6d9466faa41b39c5a4fa669": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HBoxModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HBoxModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HBoxView",
+      "box_style": "",
+      "children": [
+       "IPY_MODEL_df01abea998d42eaaa701105782abba0",
+       "IPY_MODEL_b9d9b8a937964b85a66e8353c4d44f8d",
+       "IPY_MODEL_9cbc3634d1d746b18c09b2ca6fe61a30"
+      ],
+      "layout": "IPY_MODEL_d2c6d0d6130e49f98360959d38f74d95"
+     }
+    },
+    "f2ca64e852a94f9ba7dc4bf773a695ab": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "ProgressStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "ProgressStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "bar_color": null,
+      "description_width": ""
+     }
+    },
+    "fb8ab0c7d7af4fdc9a6f99a06c53b681": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HBoxModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HBoxModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HBoxView",
+      "box_style": "",
+      "children": [
+       "IPY_MODEL_11593c36fc804d4db38f1b156c2d9456",
+       "IPY_MODEL_2269c361542a4ad6840513d5ba598ed5",
+       "IPY_MODEL_54bd32b592f842d6ab4cbafe7486714b"
+      ],
+      "layout": "IPY_MODEL_40dc70033f81402db224255a72a90ede"
+     }
+    },
+    "fbb749170f894741a6eb818b19d576c3": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_099c6c294cfe49779b2e46932748cd7e",
+      "placeholder": "​",
+      "style": "IPY_MODEL_8112a8a0c34a474bb0229c91b0ae88e0",
+      "value": " 2.42M/2.42M [00:01&lt;00:00, 1.23MB/s]"
+     }
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/python/sparknlp/annotator/classifier_dl/__init__.py b/python/sparknlp/annotator/classifier_dl/__init__.py
index ed3e7160892349..b01c166c40999e 100644
--- a/python/sparknlp/annotator/classifier_dl/__init__.py
+++ b/python/sparknlp/annotator/classifier_dl/__init__.py
@@ -52,4 +52,5 @@
 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 *
-from sparknlp.annotator.classifier_dl.albert_for_zero_shot_classification import *
\ No newline at end of file
+from sparknlp.annotator.classifier_dl.albert_for_zero_shot_classification import *
+from sparknlp.annotator.classifier_dl.camembert_for_zero_shot_classification import *
diff --git a/python/sparknlp/annotator/classifier_dl/camembert_for_zero_shot_classification.py b/python/sparknlp/annotator/classifier_dl/camembert_for_zero_shot_classification.py
new file mode 100644
index 00000000000000..7b16c4475e5511
--- /dev/null
+++ b/python/sparknlp/annotator/classifier_dl/camembert_for_zero_shot_classification.py
@@ -0,0 +1,202 @@
+#  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 CamemBertForSequenceClassification."""
+
+from sparknlp.common import *
+
+
+class CamemBertForZeroShotClassification(AnnotatorModel,
+                                         HasCaseSensitiveProperties,
+                                         HasBatchedAnnotate,
+                                         HasClassifierActivationProperties,
+                                         HasCandidateLabelsProperties,
+                                         HasEngine,
+                                         HasMaxSentenceLengthLimit):
+    """CamemBertForZeroShotClassification using a `ModelForSequenceClassification` trained on NLI (natural language
+    inference) tasks. Equivalent of `DeBertaForSequenceClassification` 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.
+    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 = CamemBertForZeroShotClassification.pretrained() \\
+    ...     .setInputCols(["token", "document"]) \\
+    ...     .setOutputCol("label")
+    The default model is ``"camembert_zero_shot_classifier_xnli_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 = CamemBertForZeroShotClassification.pretrained() \\
+    ...     .setInputCols(["token", "document"]) \\
+    ...     .setOutputCol("multi_class") \\
+    ...     .setCaseSensitive(True)
+    ...     .setCandidateLabels(["sport", "politique", "science"])
+    >>> pipeline = Pipeline().setStages([
+    ...     documentAssembler,
+    ...     tokenizer,
+    ...     sequenceClassifier
+    ... ])
+    >>> data = spark.createDataFrame([["L'équipe de France joue aujourd'hui au Parc des Princes"]]).toDF("text")
+    >>> result = pipeline.fit(data).transform(data)
+    >>> result.select("class.result").show(truncate=False)
+    +------+
+    |result|
+    +------+
+    |[sport]|
+    +------+
+    """
+    name = "CamemBertForZeroShotClassification"
+
+    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, by default True.
+
+        Due to max sequence length limit in almost all transformer models such as BERT
+        (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.
+
+        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.CamemBertForZeroShotClassification",
+                 java_model=None):
+        super(CamemBertForZeroShotClassification, 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
+        -------
+        CamemBertForZeroShotClassification
+            The restored model
+        """
+        from sparknlp.internal import _CamemBertForZeroShotClassificationLoader
+        jModel = _CamemBertForZeroShotClassificationLoader(folder, spark_session._jsparkSession)._java_obj
+        return CamemBertForZeroShotClassification(java_model=jModel)
+
+    @staticmethod
+    def pretrained(name="camembert_zero_shot_classifier_xnli_onnx", lang="fr", remote_loc=None):
+        """Downloads and loads a pretrained model.
+
+        Parameters
+        ----------
+        name : str, optional
+            Name of the pretrained model, by default
+            "camembert_zero_shot_classifier_xnli_onnx"
+        lang : str, optional
+            Language of the pretrained model, by default "fr"
+        remote_loc : str, optional
+            Optional remote address of the resource, by default None. Will use
+            Spark NLPs repositories otherwise.
+
+        Returns
+        -------
+        CamemBertForSequenceClassification
+            The restored model
+        """
+        from sparknlp.pretrained import ResourceDownloader
+        return ResourceDownloader.downloadModel(CamemBertForZeroShotClassification, name, lang, remote_loc)
diff --git a/python/sparknlp/annotator/classifier_dl/deberta_for_zero_shot_classification.py b/python/sparknlp/annotator/classifier_dl/deberta_for_zero_shot_classification.py
index 51b04f47d39ee8..6e8364cf8caaf4 100644
--- a/python/sparknlp/annotator/classifier_dl/deberta_for_zero_shot_classification.py
+++ b/python/sparknlp/annotator/classifier_dl/deberta_for_zero_shot_classification.py
@@ -21,7 +21,8 @@ class DeBertaForZeroShotClassification(AnnotatorModel,
                                           HasBatchedAnnotate,
                                           HasClassifierActivationProperties,
                                           HasCandidateLabelsProperties,
-                                          HasEngine):
+                                          HasEngine,
+                                          HasMaxSentenceLengthLimit):
     """DeBertaForZeroShotClassification using a `ModelForSequenceClassification` trained on NLI (natural language
     inference) tasks. Equivalent of `DeBertaForSequenceClassification` 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
@@ -101,11 +102,6 @@ class per document by averaging probabilities in all sentences, by
 
     outputAnnotatorType = AnnotatorType.CATEGORY
 
-    maxSentenceLength = Param(Params._dummy(),
-                              "maxSentenceLength",
-                              "Max sentence length to process",
-                              typeConverter=TypeConverters.toInt)
-
     configProtoBytes = Param(Params._dummy(),
                              "configProtoBytes",
                              "ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()",
@@ -130,15 +126,6 @@ def setConfigProtoBytes(self, b):
         """
         return self._set(configProtoBytes=b)
 
-    def setMaxSentenceLength(self, value):
-        """Sets max sentence length to process, by default 128.
-        Parameters
-        ----------
-        value : int
-            Max sentence length to process
-        """
-        return self._set(maxSentenceLength=value)
-
     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 DeBerta
diff --git a/python/sparknlp/internal/__init__.py b/python/sparknlp/internal/__init__.py
index adf19667279fbe..c8732ef3ecb4e5 100644
--- a/python/sparknlp/internal/__init__.py
+++ b/python/sparknlp/internal/__init__.py
@@ -860,6 +860,13 @@ def __init__(self, path, jspark):
             jspark,
         )
 
+class _CamemBertForZeroShotClassificationLoader(ExtendedJavaWrapper):
+    def __init__(self, path, jspark):
+        super(_CamemBertForZeroShotClassificationLoader, self).__init__(
+            "com.johnsnowlabs.nlp.annotators.classifier.dl.CamemBertForZeroShotClassification.loadSavedModel",
+            path,
+            jspark,
+        )
 
 class _RobertaQAToZeroShotNerLoader(ExtendedJavaWrapper):
     def __init__(self, path):
diff --git a/python/test/annotator/classifier_dl/camembert_for_zero_shot_classification_test.py b/python/test/annotator/classifier_dl/camembert_for_zero_shot_classification_test.py
new file mode 100644
index 00000000000000..57f1bc8256f2ce
--- /dev/null
+++ b/python/test/annotator/classifier_dl/camembert_for_zero_shot_classification_test.py
@@ -0,0 +1,57 @@
+#  Copyright 2017-2022 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 os
+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 CamemBertForZeroShotClassificationTestSpec(unittest.TestCase, HasMaxSentenceLengthTests):
+    def setUp(self):
+        self.text = "L'équipe de France joue aujourd'hui au Parc des Princes"
+        self.data = SparkContextForTest.spark \
+            .createDataFrame([[self.text]]).toDF("text")
+
+        self.tested_annotator = CamemBertForZeroShotClassification \
+            .pretrained() \
+            .setInputCols(["document", "token"]) \
+            .setOutputCol("class")
+
+    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)
+        print(annotations_result)
diff --git a/src/main/scala/com/johnsnowlabs/ml/ai/CamemBertClassification.scala b/src/main/scala/com/johnsnowlabs/ml/ai/CamemBertClassification.scala
index aa2eac4270f4c9..3b2d7ef2c7663d 100644
--- a/src/main/scala/com/johnsnowlabs/ml/ai/CamemBertClassification.scala
+++ b/src/main/scala/com/johnsnowlabs/ml/ai/CamemBertClassification.scala
@@ -16,15 +16,16 @@
 
 package com.johnsnowlabs.ml.ai
 
-import ai.onnxruntime.OnnxTensor
+import ai.onnxruntime.{OnnxTensor, OrtEnvironment}
 import com.johnsnowlabs.ml.onnx.{OnnxSession, OnnxWrapper}
 import com.johnsnowlabs.ml.tensorflow.sentencepiece.{SentencePieceWrapper, SentencepieceEncoder}
 import com.johnsnowlabs.ml.tensorflow.sign.{ModelSignatureConstants, ModelSignatureManager}
 import com.johnsnowlabs.ml.tensorflow.{TensorResources, TensorflowWrapper}
 import com.johnsnowlabs.ml.util.{ONNX, TensorFlow}
 import com.johnsnowlabs.nlp.annotators.common._
+import com.johnsnowlabs.nlp.annotators.tokenizer.wordpiece.BasicTokenizer
 import com.johnsnowlabs.nlp.{ActivationFunction, Annotation}
-import org.tensorflow.ndarray.buffer.LongDataBuffer
+import org.tensorflow.ndarray.buffer.{IntDataBuffer, LongDataBuffer}
 import org.slf4j.{Logger, LoggerFactory}
 
 import scala.collection.JavaConverters._
@@ -97,7 +98,19 @@ private[johnsnowlabs] class CamemBertClassification(
   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],
@@ -142,6 +155,7 @@ private[johnsnowlabs] class CamemBertClassification(
 
   private def getRawScoresWithTF(batch: Seq[Array[Int]], maxSentenceLength: Int): Array[Float] = {
     val tensors = new TensorResources()
+//    val (tokenBuffers, maskBuffers) = initializeTFLongTensorResources(batch, tensors, maxSentenceLength)
 
     val maxSentenceLength = batch.map(encodedSentence => encodedSentence.length).max
     val batchLength = batch.length
@@ -192,17 +206,8 @@ private[johnsnowlabs] class CamemBertClassification(
   }
 
   private def getRawScoresWithOnnx(batch: Seq[Array[Int]]): Array[Float] = {
-
-    // [nb of encoded sentences , maxSentenceLength]
     val (runner, env) = onnxWrapper.get.getSession(onnxSessionOptions)
-
-    val tokenTensors =
-      OnnxTensor.createTensor(env, batch.map(x => x.map(x => x.toLong)).toArray)
-    val maskTensors =
-      OnnxTensor.createTensor(
-        env,
-        batch.map(sentence => sentence.map(x => if (x == 0L) 0L else 1L)).toArray)
-
+    val (tokenTensors, maskTensors) = initializeOnnxTensorResources(batch, env)
     val inputs =
       Map("input_ids" -> tokenTensors, "attention_mask" -> maskTensors).asJava
 
@@ -260,13 +265,114 @@ private[johnsnowlabs] class CamemBertClassification(
       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 => computeZeroShotLogitsWithTF(paddedBatch, maxSentenceLength)
+      case ONNX.name => computeZeroShotLogitsWithONNX(paddedBatch)
+    }
+
+    val dim = rawScores.length / batchLength
+    rawScores
+      .grouped(dim)
+      .toArray
+  }
+
+  def computeZeroShotLogitsWithONNX(batch: Seq[Array[Int]]): Array[Float] = {
+    val (runner, env) = onnxWrapper.get.getSession(onnxSessionOptions)
+    val (tokenTensors, maskTensors) = initializeOnnxTensorResources(batch, env)
+    val inputs =
+      Map("input_ids" -> tokenTensors, "attention_mask" -> maskTensors).asJava
+
+    try {
+      val results = runner.run(inputs)
+      try {
+        val embeddings = results
+          .get("logits")
+          .get()
+          .asInstanceOf[OnnxTensor]
+          .getFloatBuffer
+          .array()
+        tokenTensors.close()
+        maskTensors.close()
+
+        embeddings
+      } finally if (results != null) results.close()
+    }
+  }
+
+  def computeZeroShotLogitsWithTF(
+      batch: Seq[Array[Int]],
+      maxSentenceLength: Int): Array[Float] = {
+    val tensors = new TensorResources()
+    val (tokenBuffers, maskBuffers, segmentBuffers) =
+      initializeTFIntTensorResources(batch, tensors, maxSentenceLength)
+    // [nb of encoded sentences , maxSentenceLength]
+    val shape = Array(batch.length.toLong, maxSentenceLength)
+
+    batch.zipWithIndex
+      .foreach { case (sentence, idx) =>
+        val offset = idx * maxSentenceLength
+        tokenBuffers.offset(offset).write(sentence)
+        maskBuffers
+          .offset(offset)
+          .write(sentence.map(x => if (x == sentencePadTokenId) 0 else 1))
+        segmentBuffers.offset(offset).write(Array.fill(maxSentenceLength)(0))
+      }
+
+    val runner = tensorflowWrapper.get
+      .getTFSessionWithSignature(configProtoBytes = configProtoBytes, initAllTables = false)
+      .runner
+
+    val tokenTensors = tensors.createIntBufferTensor(shape, tokenBuffers)
+    val maskTensors = tensors.createIntBufferTensor(shape, maskBuffers)
+    val segmentTensors = tensors.createIntBufferTensor(shape, segmentBuffers)
+
+    runner
+      .feed(
+        _tfCamemBertSignatures.getOrElse(
+          ModelSignatureConstants.InputIds.key,
+          "missing_input_id_key"),
+        tokenTensors)
+      .feed(
+        _tfCamemBertSignatures
+          .getOrElse(ModelSignatureConstants.AttentionMask.key, "missing_input_mask_key"),
+        maskTensors)
+      .feed(
+        _tfCamemBertSignatures
+          .getOrElse(ModelSignatureConstants.TokenTypeIds.key, "missing_segment_ids_key"),
+        segmentTensors)
+      .fetch(_tfCamemBertSignatures
+        .getOrElse(ModelSignatureConstants.LogitsOutput.key, "missing_logits_key"))
+
+    val outs = runner.run().asScala
+    val rawScores = TensorResources.extractFloats(outs.head)
+
+    outs.foreach(_.close())
+    tensors.clearSession(outs)
+    tensors.clearTensors()
+
+    rawScores
+  }
+
+  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
+    val maxSentenceLength = batch.map(encodedSentence => encodedSentence.length).max
     val (startLogits, endLogits) = detectedEngine match {
       case ONNX.name => computeLogitsWithOnnx(batch)
-      case _ => computeLogitsWithTF(batch)
+      case TensorFlow.name => computeLogitsWithTF(batch, maxSentenceLength)
     }
 
     val endDim = endLogits.length / batchLength
@@ -280,14 +386,12 @@ private[johnsnowlabs] class CamemBertClassification(
     (startScores, endScores)
   }
 
-  private def computeLogitsWithTF(batch: Seq[Array[Int]]): (Array[Float], Array[Float]) = {
+  private def computeLogitsWithTF(
+      batch: Seq[Array[Int]],
+      maxSentenceLength: Int): (Array[Float], Array[Float]) = {
     val tensors = new TensorResources()
-
-    val maxSentenceLength = batch.map(encodedSentence => encodedSentence.length).max
-    val batchLength = batch.length
-
-    val tokenBuffers: LongDataBuffer = tensors.createLongBuffer(batchLength * maxSentenceLength)
-    val maskBuffers: LongDataBuffer = tensors.createLongBuffer(batchLength * maxSentenceLength)
+    val (tokenBuffers, maskBuffers) =
+      initializeTFLongTensorResources(batch, tensors, maxSentenceLength)
 
     // [nb of encoded sentences , maxSentenceLength]
     val shape = Array(batch.length.toLong, maxSentenceLength)
@@ -335,17 +439,32 @@ private[johnsnowlabs] class CamemBertClassification(
     (startLogits, endLogits)
   }
 
-  private def computeLogitsWithOnnx(batch: Seq[Array[Int]]): (Array[Float], Array[Float]) = {
-    // [nb of encoded sentences]
-    val (runner, env) = onnxWrapper.get.getSession(onnxSessionOptions)
+  private def initializeTFLongTensorResources(
+      batch: Seq[Array[Int]],
+      tensors: TensorResources,
+      maxSentenceLength: Int): (LongDataBuffer, LongDataBuffer) = {
+    val batchLength = batch.length
+    val dim = batchLength * maxSentenceLength
+    val tokenBuffers: LongDataBuffer = tensors.createLongBuffer(dim)
+    val maskBuffers: LongDataBuffer = tensors.createLongBuffer(dim)
+    (tokenBuffers, maskBuffers)
+  }
 
-    val tokenTensors =
-      OnnxTensor.createTensor(env, batch.map(x => x.map(x => x.toLong)).toArray)
-    val maskTensors =
-      OnnxTensor.createTensor(
-        env,
-        batch.map(sentence => sentence.map(x => if (x == 0L) 0L else 1L)).toArray)
+  private def initializeTFIntTensorResources(
+      batch: Seq[Array[Int]],
+      tensors: TensorResources,
+      maxSentenceLength: Int): (IntDataBuffer, IntDataBuffer, IntDataBuffer) = {
+    val batchLength = batch.length
+    val dim = batchLength * maxSentenceLength
+    val tokenBuffers: IntDataBuffer = tensors.createIntBuffer(dim)
+    val maskBuffers: IntDataBuffer = tensors.createIntBuffer(dim)
+    val segmentBuffers: IntDataBuffer = tensors.createIntBuffer(dim)
+    (tokenBuffers, maskBuffers, segmentBuffers)
+  }
 
+  private def computeLogitsWithOnnx(batch: Seq[Array[Int]]): (Array[Float], Array[Float]) = {
+    val (runner, env) = onnxWrapper.get.getSession(onnxSessionOptions)
+    val (tokenTensors, maskTensors) = initializeOnnxTensorResources(batch, env)
     val inputs =
       Map("input_ids" -> tokenTensors, "attention_mask" -> maskTensors).asJava
 
@@ -380,6 +499,17 @@ private[johnsnowlabs] class CamemBertClassification(
     }
   }
 
+  private def initializeOnnxTensorResources(batch: Seq[Array[Int]], env: OrtEnvironment) = {
+    val tokenTensors =
+      OnnxTensor.createTensor(env, batch.map(x => x.map(x => x.toLong)).toArray)
+    val maskTensors =
+      OnnxTensor.createTensor(
+        env,
+        batch.map(sentence => sentence.map(x => if (x == 0L) 0L else 1L)).toArray)
+
+    (tokenTensors, maskTensors)
+  }
+
   def findIndexedToken(
       tokenizedSentences: Seq[TokenizedSentence],
       sentence: (WordpieceTokenizedSentence, Int),
diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotator.scala b/src/main/scala/com/johnsnowlabs/nlp/annotator.scala
index 1b46ec8330bc48..36cd023551e263 100644
--- a/src/main/scala/com/johnsnowlabs/nlp/annotator.scala
+++ b/src/main/scala/com/johnsnowlabs/nlp/annotator.scala
@@ -552,6 +552,9 @@ package object annotator {
 
   object CamemBertEmbeddings extends ReadablePretrainedCamemBertModel with ReadCamemBertDLModel
 
+  type CamemBertaForZeroShotClassification =
+    com.johnsnowlabs.nlp.annotators.classifier.dl.CamemBertForZeroShotClassification
+
   type SpanBertCorefModel = com.johnsnowlabs.nlp.annotators.coref.SpanBertCorefModel
 
   object SpanBertCorefModel
diff --git a/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/CamemBertForZeroShotClassification.scala b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/CamemBertForZeroShotClassification.scala
new file mode 100644
index 00000000000000..4a5bcde0e87ef1
--- /dev/null
+++ b/src/main/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/CamemBertForZeroShotClassification.scala
@@ -0,0 +1,415 @@
+/*
+ * 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.CamemBertClassification
+import com.johnsnowlabs.ml.onnx.{OnnxWrapper, ReadOnnxModel, WriteOnnxModel}
+import com.johnsnowlabs.ml.tensorflow.{
+  ReadTensorflowModel,
+  TensorflowWrapper,
+  WriteTensorflowModel
+}
+import com.johnsnowlabs.ml.tensorflow.sentencepiece.{
+  ReadSentencePieceModel,
+  SentencePieceWrapper,
+  WriteSentencePieceModel
+}
+import com.johnsnowlabs.ml.util.LoadExternalModel.{
+  loadSentencePieceAsset,
+  loadTextAsset,
+  modelSanityCheck,
+  notSupportedEngineError
+}
+import com.johnsnowlabs.ml.util.{ONNX, TensorFlow}
+import com.johnsnowlabs.nlp.annotators.common.{SentenceSplit, TokenizedWithSentence}
+import com.johnsnowlabs.nlp.serialization.MapFeature
+import com.johnsnowlabs.nlp.{
+  Annotation,
+  AnnotatorModel,
+  AnnotatorType,
+  HasBatchedAnnotate,
+  HasCandidateLabelsProperties,
+  HasCaseSensitiveProperties,
+  HasClassifierActivationProperties,
+  HasEngine,
+  HasPretrained,
+  ParamsAndFeaturesReadable
+}
+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 CamemBertForZeroShotClassification(override val uid: String)
+    extends AnnotatorModel[CamemBertForZeroShotClassification]
+    with HasBatchedAnnotate[CamemBertForZeroShotClassification]
+    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("CamemBertForZeroShotClassification"))
+
+  /** Input Annotator Types: DOCUMENT, TOKEN
+    *
+    * @group anno
+    */
+  override val inputAnnotatorTypes: Array[String] =
+    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 = set(labels, value)
+
+  /** 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]): CamemBertForZeroShotClassification.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[CamemBertClassification]] = None
+
+  /** @group setParam */
+  def setModelIfNotSet(
+      spark: SparkSession,
+      tensorflowWrapper: Option[TensorflowWrapper],
+      onnxWrapper: Option[OnnxWrapper],
+      spp: SentencePieceWrapper): CamemBertForZeroShotClassification = {
+    if (_model.isEmpty) {
+      _model = Some(
+        spark.sparkContext.broadcast(
+          new CamemBertClassification(
+            tensorflowWrapper,
+            onnxWrapper,
+            spp,
+            configProtoBytes = None,
+            tags = $$(labels),
+            signatures = getSignatures,
+            threshold = $(threshold))))
+    }
+
+    this
+  }
+
+  /** Whether to lowercase tokens or not (Default: `true`).
+    *
+    * @group setParam
+    */
+  override def setCaseSensitive(value: Boolean): this.type = {
+    set(this.caseSensitive, value)
+  }
+
+  /** @group getParam */
+  def getModelIfNotSet: CamemBertClassification = _model.get.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 = "_camembert_classification"
+
+    getEngine match {
+      case TensorFlow.name =>
+        writeTensorflowModelV2(
+          path,
+          spark,
+          getModelIfNotSet.tensorflowWrapper.get,
+          suffix,
+          CamemBertForSequenceClassification.tfFile)
+      case ONNX.name =>
+        writeOnnxModel(
+          path,
+          spark,
+          getModelIfNotSet.onnxWrapper.get,
+          suffix,
+          CamemBertForSequenceClassification.onnxFile)
+    }
+
+    writeSentencePieceModel(
+      path,
+      spark,
+      getModelIfNotSet.spp,
+      "_camembert",
+      CamemBertForSequenceClassification.sppFile)
+
+  }
+}
+
+trait ReadPretrainedCamemBertForZeroShotClassification
+    extends ParamsAndFeaturesReadable[CamemBertForZeroShotClassification]
+    with HasPretrained[CamemBertForZeroShotClassification] {
+  override val defaultModelName: Some[String] = Some("camembert_zero_shot_classifier_xnli_onnx")
+  override val defaultLang: String = "fr"
+
+  override def pretrained(): CamemBertForZeroShotClassification = super.pretrained()
+
+  override def pretrained(name: String): CamemBertForZeroShotClassification =
+    super.pretrained(name)
+
+  override def pretrained(name: String, lang: String): CamemBertForZeroShotClassification =
+    super.pretrained(name, lang)
+
+  override def pretrained(
+      name: String,
+      lang: String,
+      remoteLoc: String): CamemBertForZeroShotClassification =
+    super.pretrained(name, lang, remoteLoc)
+}
+
+trait ReadCamemBertForZeroShotClassification
+    extends ReadTensorflowModel
+    with ReadOnnxModel
+    with ReadSentencePieceModel {
+  this: ParamsAndFeaturesReadable[CamemBertForZeroShotClassification] =>
+
+  override val tfFile: String = "camembert_classification_tensorflow"
+  override val onnxFile: String = "camembert_classification_onnx"
+  override val sppFile: String = "camembert_spp"
+
+  def readModel(
+      instance: CamemBertForZeroShotClassification,
+      path: String,
+      spark: SparkSession): Unit = {
+
+    val spp = readSentencePieceModel(path, spark, "_camembert_spp", sppFile)
+
+    instance.getEngine match {
+      case TensorFlow.name =>
+        val tfWrapper = readTensorflowModel(path, spark, "_camembert_classification_tf")
+        instance.setModelIfNotSet(spark, Some(tfWrapper), None, spp)
+      case ONNX.name =>
+        val onnxWrapper =
+          readOnnxModel(
+            path,
+            spark,
+            "camembert_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): CamemBertForZeroShotClassification = {
+
+    val (localModelPath, detectedEngine) = modelSanityCheck(modelPath)
+
+    val spModel = loadSentencePieceAsset(localModelPath, "sentencepiece.bpe.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 CamemBertForZeroShotClassification()
+      .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 [[CamemBertForZeroShotClassification]]. Please refer to that
+  * class for the documentation.
+  */
+
+object CamemBertForZeroShotClassification
+    extends ReadPretrainedCamemBertForZeroShotClassification
+    with ReadCamemBertForZeroShotClassification
diff --git a/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala b/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala
index 8ed41de985baa9..c4be567a26683b 100644
--- a/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala
+++ b/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala
@@ -687,7 +687,8 @@ object PythonResourceDownloader {
     "AutoGGUFModel" -> AutoGGUFModel,
     "AlbertForZeroShotClassification" -> AlbertForZeroShotClassification,
     "MxbaiEmbeddings" -> MxbaiEmbeddings,
-    "SnowFlakeEmbeddings" -> SnowFlakeEmbeddings
+    "SnowFlakeEmbeddings" -> SnowFlakeEmbeddings,
+    "CamemBertForZeroShotClassification" -> CamemBertForZeroShotClassification
   )
 
   // List pairs of types such as the one with key type can load a pretrained model from the value type
diff --git a/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceMetadata.scala b/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceMetadata.scala
index 4f53b37db985c2..992708e86c0992 100644
--- a/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceMetadata.scala
+++ b/src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceMetadata.scala
@@ -35,7 +35,8 @@ case class ResourceMetadata(
     isZipped: Boolean = false,
     category: Option[String] = Some(ResourceType.NOT_DEFINED.toString),
     checksum: String = "",
-    annotator: Option[String] = None)
+    annotator: Option[String] = None,
+    engine: Option[String] = None)
     extends Ordered[ResourceMetadata] {
 
   lazy val key: String = {
diff --git a/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/CamemBertForZeroShotClassificationTestSpec.scala b/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/CamemBertForZeroShotClassificationTestSpec.scala
new file mode 100644
index 00000000000000..af9f114920a1ec
--- /dev/null
+++ b/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/CamemBertForZeroShotClassificationTestSpec.scala
@@ -0,0 +1,99 @@
+/*
+ * 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 CamemBertForZeroShotClassificationTestSpec extends AnyFlatSpec {
+
+  "CamemBertForZeroShotClassification" should "correctly load custom ONNX model" taggedAs SlowTest in {
+    import ResourceHelper.spark.implicits._
+
+    val dataDf = Seq("L'équipe de France joue aujourd'hui au Parc des Princes").toDF("text")
+
+    val document = new DocumentAssembler()
+      .setInputCol("text")
+      .setOutputCol("document")
+
+    val tokenizer = new Tokenizer()
+      .setInputCols(Array("document"))
+      .setOutputCol("token")
+
+    val zeroShotClassifier = CamemBertForZeroShotClassification
+      .pretrained()
+      .setOutputCol("multi_class")
+      .setCaseSensitive(true)
+      .setCoalesceSentences(true)
+      .setCandidateLabels(Array("sport", "politique", "science"))
+
+    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: $totalLabels")
+
+    assert(totalDocs == totalLabels)
+  }
+
+  it should "correctly load custom Tensorflow model" taggedAs SlowTest in {
+    import ResourceHelper.spark.implicits._
+
+    val dataDf = Seq("L'équipe de France joue aujourd'hui au Parc des Princes").toDF("text")
+
+    val document = new DocumentAssembler()
+      .setInputCol("text")
+      .setOutputCol("document")
+
+    val tokenizer = new Tokenizer()
+      .setInputCols(Array("document"))
+      .setOutputCol("token")
+
+    val zeroShotClassifier = CamemBertForZeroShotClassification
+      .pretrained("camembert-zero-shot-classifier-xnli-tf")
+      .setOutputCol("multi_class")
+      .setCaseSensitive(true)
+      .setCoalesceSentences(true)
+      .setCandidateLabels(Array("sport", "politique", "science"))
+
+    val pipeline = new Pipeline().setStages(Array(document, tokenizer, zeroShotClassifier))
+
+    val pipelineModel = pipeline.fit(dataDf)
+    val pipelineDF = pipelineModel.transform(dataDf)
+
+    pipelineDF.select("multi_class").show(false)
+    val totalDocs = pipelineDF.select(explode($"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)
+  }
+
+}