diff --git a/examples/python/transformers/openvino/HuggingFace_OpenVINO_in_Spark_NLP_BERT.ipynb b/examples/python/transformers/openvino/HuggingFace_OpenVINO_in_Spark_NLP_BERT.ipynb new file mode 100644 index 00000000000000..5bfc2356ad4516 --- /dev/null +++ b/examples/python/transformers/openvino/HuggingFace_OpenVINO_in_Spark_NLP_BERT.ipynb @@ -0,0 +1,2375 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "_V5XcDCnVgSi" + }, + "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/openvino/HuggingFace_OpenVINO_in_Spark_NLP_BERT.ipynb)\n", + "\n", + "# Import OpenVINO BERT models from HuggingFace 🤗 into Spark NLP 🚀\n", + "\n", + "This notebook provides a detailed walkthrough on optimizing and exporting BERT models from HuggingFace for use in Spark NLP, leveraging the various tools provided in the [Intel OpenVINO toolkit](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html) ecosystem.\n", + "\n", + "Let's keep in mind a few things before we start 😊\n", + "\n", + "- OpenVINO support was introduced in `Spark NLP 5.4.0`, enabling high performance inference for models. Please make sure you have upgraded to the latest Spark NLP release.\n", + "- You can import models for BERT from HuggingFace and they have to be in `Fill Mask` category. Meaning, you cannot use BERT models trained/fine-tuned on a specific task such as token/sequence classification." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aghasVppVgSk" + }, + "source": [ + "## 1. Export and Save the HuggingFace model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "be4HsTDMVgSk" + }, + "source": [ + "- Let's install `transformers` and `openvino` packages with other dependencies. You don't need `openvino` to be installed for Spark NLP, however, we need it to load and save models from HuggingFace.\n", + "- We lock `transformers` on version `4.41.2`. This doesn't mean it won't work with the future releases, but we wanted you to know which versions have been tested successfully." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-7L-2ZWUVgSl", + "outputId": "12404fa1-7ed6-4007-dc38-e11b440a095e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.1/9.1 MB\u001b[0m \u001b[31m22.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m215.7/215.7 kB\u001b[0m \u001b[31m19.1 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[31m62.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m38.7/38.7 MB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m418.4/418.4 kB\u001b[0m \u001b[31m32.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.1/542.1 kB\u001b[0m \u001b[31m37.7 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[31m38.0 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[31m9.8 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[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m11.7 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[31m3.9 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[31m50.0 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[31m10.1 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", + "google-ai-generativelanguage 0.6.4 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n", + "google-api-core 2.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0.dev0,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n", + "google-cloud-aiplatform 1.52.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n", + "google-cloud-bigquery-connection 1.12.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n", + "google-cloud-bigquery-storage 2.25.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n", + "google-cloud-datastore 2.15.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n", + "google-cloud-firestore 2.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n", + "google-cloud-functions 1.13.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n", + "google-cloud-iam 2.15.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n", + "google-cloud-language 2.13.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n", + "google-cloud-resource-manager 1.12.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n", + "google-cloud-translate 3.11.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 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 1.63.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0.dev0,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n", + "grpc-google-iam-v1 0.13.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n", + "tensorflow 2.15.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.20.1 which is incompatible.\n", + "tensorflow-metadata 1.15.0 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 3.20.1 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install -q --upgrade transformers==4.41.2\n", + "!pip install -q --upgrade openvino==2024.1\n", + "!pip install -q --upgrade optimum-intel==1.17.0\n", + "!pip install -q --upgrade onnx==1.12.0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vI7uz_6hVgSl" + }, + "source": [ + "[Optimum Intel](https://github.com/huggingface/optimum-intel?tab=readme-ov-file#openvino) is the interface between the Transformers library and the various model optimization and acceleration tools provided by Intel. HuggingFace models loaded with optimum-intel are automatically optimized for OpenVINO, while being compatible with the Transformers API.\n", + "- To load a HuggingFace model directly for inference/export, just replace the `AutoModelForXxx` class with the corresponding `OVModelForXxx` class. We can use this to import and export OpenVINO models with `from_pretrained` and `save_pretrained`.\n", + "- By setting `export=True`, the source model is converted to OpenVINO IR format on the fly.\n", + "- We'll use [bert-base-cased](https://huggingface.co/bert-base-cased) model from HuggingFace as an example and load it as a `OVModelForFeatureExtraction`, representing an OpenVINO model.\n", + "- In addition to the BERT model, we also need to save the `BertTokenizer`. This is the same for every model, these are assets (saved in `/assets`) needed for tokenization inside Spark NLP." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452, + "referenced_widgets": [ + "01b5c1c2234a44e099d7f465713bbb38", + "25b6b7d59e1e4c39951ab02c23c33e6e", + "37d2d149ee284daeaef8eb5be07f24d3", + "9e9fae5b6a8247fd8d347056ef643306", + "6cbfd3b4af1147c08a2214dbf8c7a47d", + "aad716fed08d4d90899e3b1787fb501e", + "eaa2920387434985a5b9935d52d090e2", + "11f40d7cd8d54b7ba390936893636273", + "6ed1ff0cd44d4665bc02b6dfe02b230a", + "bfaf1399c45a457894a6a7e7dc20e1cd", + "a16fe8bed4fd40f7a95dbdc084358c92", + "0f9f28b5c5f64ed49572eaf86b382994", + "7720df091cdd47f1b6bc68a858eca368", + "75a7b1d34c7b45e0b52e625c3f901de0", + "97a940fe653a4bbe80e725a624a4c852", + "2e0666487b784392a70f63973a20752a", + "8eab39042cb8498ba7d4a0862db042a3", + "7951e69e6f514a0d980a7540ad13aeda", + "762915c7155745c8857385e06c112f32", + "3671c6d423df4acea15c8eaa9a32a64b", + "8d5f7ba166d349d299a83e9aa87d7e8e", + "a5e6fd1e759f4d8eb3626204abb28368", + "233dd14b19074f26ae35f57a0074cfe1", + "480ae166d1664bdd8bda16c3d58e9111", + "44e8e13d44494ce08b3a827e57f9132f", + "e4747daefcbc478e845a0a6f5719c9c1", + "2e8d78704b884f328db23de1825a5220", + "d51943feb0c840ceab5736dc14897e43", + "5ca883e9477a4596bf678a943af4fe7e", + "788490ad0e2a4d319be7249c02936e17", + "7c1da73731d64344a72334134d26fa3d", + "0cf51e003d18431e8e45492e3456616d", + "251f23d6f955463e896df5c811672203", + "6a68e4c10df04c5f9ab78219609e3fbd", + "6d7b76e252ea41279edc13722956d5a6", + "384b3bb9f8a048e78717f807ac827f87", + "33d7c15703664532852a342cd4e2d104", + "e1e834f926484660b5084a7b560f799c", + "c102bbf6f21d42fda00e657dc59e0989", + "4e799a305854404281e03fc668efaf3f", + "f510017f15cf4a8dbcb628463536a839", + "54496018237d47c0b1f4cd55626ac722", + "48810a089dd74df2af0c2876e0f27c5a", + "de38c18e08334ba18d486b6876ffdfaa", + "fe651ea5472d4af7a21488ab12990af8", + "843a43bc994c496399b257f73f2c0d65", + "a9864a5f26464288b34fc1d34b416a73", + "04e210f78ca14c99bfbed0d5e92bea73", + "f24373ab9de644bfbadc6dedfdf7cdab", + "76faaeb895dc4c18abdb02d0e92443f5", + "b536cd78742c40f4a538fca347611d3c", + "e00dadec26bf4ad6bee9308024658856", + "c9baefa2669a4e4fbe8e7a70c18ca560", + "ac6cee0cdb21438792aab6fd63963bf4", + "e71cf1ead3c044f1b4fa4bd6cae16010" + ] + }, + "id": "qF5Pp3DuVgSm", + "outputId": "ac2daa37-bd17-4aad-a4c3-ca1633e6f35d" + }, + "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", + "/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": "01b5c1c2234a44e099d7f465713bbb38", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "config.json: 0%| | 0.00/570 [00:00 False\n", + "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead\n", + " warnings.warn(\n", + "Compiling the model to CPU ...\n" + ] + } + ], + "source": [ + "from optimum.intel import OVModelForFeatureExtraction\n", + "from transformers import BertTokenizer\n", + "\n", + "MODEL_NAME = \"bert-base-cased\"\n", + "EXPORT_PATH = f\"ov_models/{MODEL_NAME}\"\n", + "\n", + "ov_model = OVModelForFeatureExtraction.from_pretrained(MODEL_NAME, export=True)\n", + "tokenizer = BertTokenizer.from_pretrained(MODEL_NAME, export=True)\n", + "\n", + "# Save the OpenVINO model\n", + "ov_model.save_pretrained(EXPORT_PATH)\n", + "tokenizer.save_pretrained(EXPORT_PATH)\n", + "\n", + "# Create directory for assets and move the tokenizer files.\n", + "# A separate folder is needed for Spark NLP.\n", + "!mkdir {EXPORT_PATH}/assets\n", + "!mv {EXPORT_PATH}/vocab.txt {EXPORT_PATH}/assets/" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BAs-wq_wVgSn" + }, + "source": [ + "Let's have a look inside these two directories and see what we are dealing with:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VKQESNCsVgSn", + "outputId": "0d175282-b9aa-4092-d372-d2974fe61fab" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 421188\n", + "drwxr-xr-x 2 root root 4096 Jun 4 00:19 assets\n", + "-rw-r--r-- 1 root root 634 Jun 4 00:19 config.json\n", + "-rw-r--r-- 1 root root 430883000 Jun 4 00:19 openvino_model.bin\n", + "-rw-r--r-- 1 root root 390155 Jun 4 00:19 openvino_model.xml\n", + "-rw-r--r-- 1 root root 125 Jun 4 00:19 special_tokens_map.json\n", + "-rw-r--r-- 1 root root 1261 Jun 4 00:19 tokenizer_config.json\n" + ] + } + ], + "source": [ + "!ls -l {EXPORT_PATH}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "daFkGhN-VgSn", + "outputId": "5562b852-c967-470a-c0f9-4fe4e2229e6b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 212\n", + "-rw-r--r-- 1 root root 213450 Jun 4 00:19 vocab.txt\n" + ] + } + ], + "source": [ + "!ls -l {EXPORT_PATH}/assets" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wu9SELxrVgSo" + }, + "source": [ + "## 2. Import and Save BERT in Spark NLP\n", + "\n", + "- 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": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qGFNUdx-VgSo", + "outputId": "5b7f1081-8abb-4657-8ed0-12c72d497e94" + }, + "outputs": [], + "source": [ + "! wget -q http://setup.johnsnowlabs.com/colab.sh -O - | bash" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OjSBCMJ8VgSo" + }, + "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": "O_-C6N41VgSo", + "outputId": "c7528c0d-cb23-463e-f41b-a07dce03b78f" + }, + "outputs": [], + "source": [ + "import sparknlp\n", + "\n", + "# let's start Spark with Spark NLP\n", + "spark = sparknlp.start()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vSkOj2TbVgSo" + }, + "source": [ + "- Let's use `loadSavedModel` functon in `BertEmbeddings` which allows us to load the OpenVINO model.\n", + "- Most params will be set automatically. They can also be set later after loading the model in `BertEmbeddings` during runtime, so don't worry about setting them now.\n", + "- `loadSavedModel` accepts two params, first is the path to the exported model. The second is the SparkSession that is `spark` variable we previously started via `sparknlp.start()`\n", + "- `setStorageRef` is very important. When you are training a task like NER or any Text Classification, we use this reference to bound the trained model to this specific embeddings so you won't load a different embeddings by mistake and see terrible results. 😊\n", + "- It's up to you what you put in `setStorageRef` but it cannot be changed later on. We usually use the name of the model to be clear, but you can get creative if you want!\n", + "- The `dimension` param is is purely cosmetic and won't change anything. It's mostly for you to know later via `.getDimension` what is the dimension of your model. So set this accordingly.\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.st 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" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "gm2YeA5SVgSo" + }, + "outputs": [], + "source": [ + "from sparknlp.annotator import *\n", + "\n", + "# All these params should be identical to the original OpenVINO model\n", + "bert = BertEmbeddings.loadSavedModel(f\"{EXPORT_PATH}\", spark)\\\n", + " .setInputCols([\"document\",'token'])\\\n", + " .setOutputCol(\"bert\")\\\n", + " .setCaseSensitive(True)\\\n", + " .setDimension(768)\\\n", + " .setStorageRef('bert_base_cased')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Vc1qgzeRVgSp" + }, + "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": 8, + "metadata": { + "id": "MPojDIHIVgSp" + }, + "outputs": [], + "source": [ + "bert.write().overwrite().save(f\"{MODEL_NAME}_spark_nlp\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9T5asBeAVgSp" + }, + "source": [ + "Let's clean up stuff we don't need anymore" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "bTP2aEHwVgSp" + }, + "outputs": [], + "source": [ + "!rm -rf {EXPORT_PATH}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cHSqqOFdVgSp" + }, + "source": [ + "Awesome 😎 !\n", + "\n", + "This is your OpenVINO BERT model from HuggingFace 🤗 loaded and saved by Spark NLP 🚀" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1RlH-qyWVgSp", + "outputId": "9856f86e-128f-4d8f-9ad3-9c49c7d43575" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 421240\n", + "-rw-r--r-- 1 root root 431339243 Jun 4 00:52 bert_openvino\n", + "drwxr-xr-x 3 root root 4096 Jun 4 00:52 fields\n", + "drwxr-xr-x 2 root root 4096 Jun 4 00:52 metadata\n" + ] + } + ], + "source": [ + "! ls -l {MODEL_NAME}_spark_nlp" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7EJHHhjkVgSp" + }, + "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 BERT model 😊" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "2sDJ42WhVgSp" + }, + "outputs": [], + "source": [ + "import sparknlp\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", + "bert_loaded = BertEmbeddings.load(f\"{MODEL_NAME}_spark_nlp\")\\\n", + " .setInputCols([\"document\",'token'])\\\n", + " .setOutputCol(\"bert\")\\\n", + "\n", + "pipeline = Pipeline(\n", + " stages = [\n", + " document_assembler,\n", + " tokenizer,\n", + " bert_loaded\n", + " ])\n", + "\n", + "data = spark.createDataFrame([['William Henry Gates III (born October 28, 1955) is an American business magnate, software developer, investor,and philanthropist.']]).toDF(\"text\")\n", + "model = pipeline.fit(data)\n", + "result = model.transform(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sQ6q4RQdVgSp", + "outputId": "949b0c1d-e95b-4c51-9d2d-026f89ab3f8b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+\n", + "| embeddings|\n", + "+--------------------+\n", + "|[0.43426424, -0.3...|\n", + "|[-0.033401597, -0...|\n", + "|[0.38291305, 0.11...|\n", + "|[-0.11996282, 0.2...|\n", + "|[-0.4832556, 0.05...|\n", + "|[-0.17415498, 0.2...|\n", + "|[0.030411722, -0....|\n", + "|[-0.09456845, -1....|\n", + "|[0.20999405, 0.27...|\n", + "|[-0.61759734, -0....|\n", + "|[0.2620508, 0.319...|\n", + "|[0.07179723, 0.31...|\n", + "|[0.11466871, 0.16...|\n", + "|[0.11231382, 0.22...|\n", + "|[0.9711217, 0.130...|\n", + "|[0.6206649, -0.10...|\n", + "|[0.21066141, 0.42...|\n", + "|[0.45186955, 0.24...|\n", + "|[0.33472046, -0.1...|\n", + "|[0.10000806, -0.3...|\n", + "+--------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "result.selectExpr(\"explode(bert.embeddings) as embeddings\").show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Siac4rcNVgSp" + }, + "source": [ + "That's it! You can now go wild and use hundreds of BERT models from HuggingFace 🤗 in Spark NLP 🚀\n" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "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": { + "01b5c1c2234a44e099d7f465713bbb38": { + "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": 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/dev/null +++ b/examples/python/transformers/openvino/HuggingFace_OpenVINO_in_Spark_NLP_E5.ipynb @@ -0,0 +1,2684 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "t3of8INfq5JR" + }, + "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/openvino/HuggingFace_OpenVINO_in_Spark_NLP_E5.ipynb)\n", + "\n", + "# Import OpenVINO E5 models from HuggingFace 🤗 into Spark NLP 🚀\n", + "\n", + "This notebook provides a detailed walkthrough on optimizing and exporting E5 models from HuggingFace for use in Spark NLP, leveraging the various tools provided in the [Intel OpenVINO toolkit](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html) ecosystem.\n", + "\n", + "Let's keep in mind a few things before we start 😊\n", + "\n", + "- OpenVINO support for this annotator was introduced in `Spark NLP 5.4.0`, enabling high performance inference for models. Please make sure you have upgraded to the latest Spark NLP release.\n", + "- You can import models for E5 from HuggingFace and they have to be in `Sentence Similarity` category. Meaning, you cannot use E5 models trained/fine-tuned on a specific task such as token/sequence classification." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Offrpi_nq5JU" + }, + "source": [ + "## 1. Export and Save HuggingFace model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AYros5kRq5JU" + }, + "source": [ + "- Let's install `transformers` and `openvino` packages with other dependencies. You don't need `openvino` to be installed for Spark NLP, however, we need it to load and save models from HuggingFace.\n", + "\n", + "- We lock `transformers` on version `4.41.2`. This doesn't mean it won't work with the future releases, but we wanted you to know which versions have been tested successfully." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "o38yt3MUq5JU", + "outputId": "d2325385-5381-4cd3-d4da-f5bb810c589c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m215.7/215.7 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m38.7/38.7 MB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m418.4/418.4 kB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.1/542.1 kB\u001b[0m \u001b[31m28.2 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[31m42.9 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[31m9.0 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[31m3.4 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[31m6.6 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[31m5.5 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[31m2.0 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[31m41.5 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[31m7.7 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", + "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install -q --upgrade transformers==4.41.2\n", + "!pip install -q --upgrade openvino==2024.1\n", + "!pip install -q --upgrade optimum-intel" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_DkxL8Raq5JW" + }, + "source": [ + "[Optimum Intel](https://github.com/huggingface/optimum-intel?tab=readme-ov-file#openvino) is the interface between the Transformers library and the various model optimization and acceleration tools provided by Intel. HuggingFace models loaded with optimum-intel are automatically optimized for OpenVINO, while being compatible with the Transformers API.\n", + "- To load a HuggingFace model directly for inference/export, just replace the `AutoModelForXxx` class with the corresponding `OVModelForXxx` class. We can use this to import and export OpenVINO models with `from_pretrained` and `save_pretrained`.\n", + "- By setting `export=True`, the source model is converted to OpenVINO IR format on the fly.\n", + "- We'll use [intfloat/e5-small-v2](https://huggingface.co/intfloat/e5-small-v2) model from HuggingFace as an example and load it as a `OVModelForFeatureExtraction`, representing an OpenVINO model.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 382, + "referenced_widgets": [ + "62ebca6fdcd74e26b2902e9e11b0fe2d", + "08a1211697e14a359ae9e27fc1d2f024", + "be0b74ba82be430f959ace545dfb2ebc", + "de340228033247e19f0285cea06ad195", + "119f28cdac064337b295de19bb42af61", + "372dd8b08cb946328861c113cd8c5298", + "e1d15d3ec7f24872b9afaa057607cce3", + "7689a9edcdd14dcfaaeae25b7047ae09", + "f252f55880f549578bd3870620b78dd9", + "11aca36f274645119b4d45de4dbdb5fc", + "38803307acc64fe99ad9fee95ab2db98", + "a3569054f2fc43028b2b7f878cddf768", + "a1f83dcb808347ba89bfb9aad1fa040f", + "eece2ff6d3bc48dcb1ba09b36c92f26a", + "f9b7b8da540d4fe19446718dd1c45d9b", + "a0e62c2c51574dedad0a7c4c6444c62e", + "76bb6075a7404d5094f81f838d32546f", + "3ac007352864466abe263a9c7ad69f62", + "b6195ad1ff294a6385386f597432b9fe", + "dddd4467d253497ca7193023749abf41", + "501bcf21cd334677bbadbf008cd7dbcf", + "22c3f946a3af4513880b04640a2b3f3f", + "049cf01cfba541a5b00e72e9b8babf15", + "ebb85c507d714b999340b13ded862249", + "a0f1629eb6764e44b65b53bf8939a923", + "93c08a1c527b4ca1b180f1b961e5b3c4", + "2aab1dc5fef943789b49b583b7683dd6", + "8b716c2622e64827961e2af5e5eb2c64", + "02a16bf5e15141389bc9f3b0451e843e", + "6c4d3962aae74148a0f570b3d541a2f7", + "2ff122725f9743f4a5ab3098a6da4318", + "defb158288f7450695d067b6bfd4a0f6", + "91fd2eb73b2f453e9f7f928a8636fae0", + "4b432cdc8ab64ecc9ba477f646c648c5", + "d50d7b8526e94dfc9f232814b4c4dae1", + "e593b4931bc74d6baa88146fb6a849a6", + "aa661d5657cb4f8ea5061c43c6c25e48", + "ff5ae5551c31480f83446a6bb139a1a2", + "3c8d49c540e044f69ac8cac387d243d8", + "711b066edbe1416b9792f7e056d09f8c", + "06a170a9e08845d99b0569711de86115", + "fd3bbd75c51e468aae962737c959435f", + "20a91d395b22401f9bec1bc661809b44", + "6b4ca1b352a5402a861757acf19943b8", + "1aa5beae9c144292a96803ea55c45bc9", + "5101cccc8e2d459094f74677ed915649", + "c89483b34e394513a5511f41a551e12f", + "65ddf687a58e4f09af21871433637c4d", + "c9c351e6de61441f95de58b02638b1e3", + "0d6d8a878cd74e15bdd96702f8d6a686", + "e31628ee239d4b1f9fd927730516d498", + "02bc174aa32942d4a5ed3fd5dd65adb4", + "f63d1a1eb33448d3a1e8371d99b084b6", + "43e9eaac48c444f2ad20a08af1c5a192", + "51d7253cc5884ff19812262712f0d89d", + "487d1ded7d904a5189b5cc33f54267c6", + "377cdd9b5cbb46b0b9015f8f8c6c7952", + "b8a0541af476474fa9d4e398af10026b", + "f78b35fd44b7440e82ba9febfe7d5b34", + "ce210f090d15462992ce790465cc7173", + "708e950a619744c59102f2386755c6f1", + "51961e0fc41e4f15a18a5e19b5a67629", + "4f50a859d6d549838c23fba630032e44", + "32dd8567e2954d3eac01223a1eab91b4", + "a9766197b71b404993fa68310519a702", + "39b88e98e4ef431fb9dcfcd5d28c8fee" + ] + }, + "id": "eEDvpru4q5JW", + "outputId": "0572538f-e62d-4970-bd05-a1a5b8a3b07d" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "62ebca6fdcd74e26b2902e9e11b0fe2d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "config.json: 0%| | 0.00/615 [00:00 False\n", + "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead\n", + " warnings.warn(\n", + "Compiling the model to CPU ...\n" + ] + } + ], + "source": [ + "from optimum.intel import OVModelForFeatureExtraction\n", + "from transformers import AutoTokenizer\n", + "\n", + "MODEL_NAME = \"intfloat/e5-small-v2\"\n", + "EXPORT_PATH = f\"ov_models/{MODEL_NAME}\"\n", + "\n", + "ov_model = OVModelForFeatureExtraction.from_pretrained(MODEL_NAME, export=True)\n", + "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n", + "\n", + "# Save the OpenVINO model\n", + "ov_model.save_pretrained(EXPORT_PATH)\n", + "tokenizer.save_pretrained(EXPORT_PATH)\n", + "\n", + "# Create directory for assets and move the tokenizer files.\n", + "# A separate folder is needed for Spark NLP.\n", + "!mkdir {EXPORT_PATH}/assets\n", + "!mv {EXPORT_PATH}/vocab.txt {EXPORT_PATH}/assets/" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UkKcnZSWq5JX" + }, + "source": [ + "Let's have a look inside these two directories and see what we are dealing with:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "s0KHaXKaq5JX", + "outputId": "ce6a3b9e-9218-41d1-c388-f4ada1aa541c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 130836\n", + "drwxr-xr-x 2 root root 4096 Jun 5 18:50 assets\n", + "-rw-r--r-- 1 root root 626 Jun 5 18:50 config.json\n", + "-rw-r--r-- 1 root root 132852920 Jun 5 18:50 openvino_model.bin\n", + "-rw-r--r-- 1 root root 389978 Jun 5 18:50 openvino_model.xml\n", + "-rw-r--r-- 1 root root 695 Jun 5 18:50 special_tokens_map.json\n", + "-rw-r--r-- 1 root root 1190 Jun 5 18:50 tokenizer_config.json\n", + "-rw-r--r-- 1 root root 711396 Jun 5 18:50 tokenizer.json\n" + ] + } + ], + "source": [ + "!ls -l {EXPORT_PATH}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_jMj2zvjq5JX", + "outputId": "ea5e828a-bf67-4c63-b184-2759520f0142" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 228\n", + "-rw-r--r-- 1 root root 231508 Jun 5 18:50 vocab.txt\n" + ] + } + ], + "source": [ + "!ls -l {EXPORT_PATH}/assets" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rbe3ooViq5JX" + }, + "source": [ + "## 2. Import and Save E5 in Spark NLP\n", + "\n", + "- 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": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "F8oT1HVzq5JY", + "outputId": "2ccff0c2-900d-4569-f9a8-239475ce0bc9" + }, + "outputs": [], + "source": [ + "! wget -q http://setup.johnsnowlabs.com/colab.sh -O - | bash" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kQymyk4pq5JY" + }, + "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": "jxRFJUwSq5JY", + "outputId": "9b2c64a4-d0cf-4f07-d247-2b2b4ea8c8b2" + }, + "outputs": [], + "source": [ + "import sparknlp\n", + "# let's start Spark with Spark NLP\n", + "spark = sparknlp.start()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hQYlhzBQq5JY" + }, + "source": [ + "- Let's use `loadSavedModel` functon in `E5Embeddings` which allows us to load the OpenVINO model.\n", + "- Most params will be set automatically. They can also be set later after loading the model in `E5Embeddings` during runtime, so don't worry about setting them now.\n", + "- `loadSavedModel` accepts two params, first is the path to the exported model. 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.st 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" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "VEcJXVo4q5JY" + }, + "outputs": [], + "source": [ + "from sparknlp.annotator import *\n", + "\n", + "E5 = E5Embeddings.loadSavedModel(f\"{EXPORT_PATH}\", spark)\\\n", + " .setInputCols([\"document\"])\\\n", + " .setOutputCol(\"E5\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AXGuTDwvq5JZ" + }, + "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": 8, + "metadata": { + "id": "jP_DX--7q5JZ" + }, + "outputs": [], + "source": [ + "E5.write().overwrite().save(f\"{MODEL_NAME}_spark_nlp\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2lmz96_9q5JZ" + }, + "source": [ + "Let's clean up stuff we don't need anymore" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "Tw7Huu33q5JZ" + }, + "outputs": [], + "source": [ + "!rm -rf {EXPORT_PATH}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P4gj2XhAq5JZ" + }, + "source": [ + "Awesome 😎 !\n", + "\n", + "This is your OpenVINO E5 model from HuggingFace 🤗 loaded and saved by Spark NLP 🚀" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fcmE8oY1q5JZ", + "outputId": "fc5a48c5-35f2-4bae-b689-b6723300f5f1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 130152\n", + "-rw-r--r-- 1 root root 133263511 Jun 5 18:53 e5_openvino\n", + "drwxr-xr-x 3 root root 4096 Jun 5 18:53 fields\n", + "drwxr-xr-x 2 root root 4096 Jun 5 18:53 metadata\n" + ] + } + ], + "source": [ + "! ls -l {MODEL_NAME}_spark_nlp" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dIV7AGvUq5JZ" + }, + "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 E5 model 😊" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "90epOB2vq5JZ" + }, + "outputs": [], + "source": [ + "import sparknlp\n", + "\n", + "from sparknlp.base import *\n", + "from sparknlp.annotator import *\n", + "\n", + "document_assembler = DocumentAssembler()\\\n", + " .setInputCol(\"text\")\\\n", + " .setOutputCol(\"document\")\n", + "\n", + "E5_loaded = E5Embeddings.load(f\"{MODEL_NAME}_spark_nlp\")\\\n", + " .setInputCols([\"document\"])\\\n", + " .setOutputCol(\"E5\")\\\n", + "\n", + "pipeline = Pipeline(\n", + " stages = [\n", + " document_assembler,\n", + " E5_loaded\n", + " ])\n", + "\n", + "data = spark.createDataFrame([['William Henry Gates III (born October 28, 1955) is an American business magnate, software developer, investor,and philanthropist.']]).toDF(\"text\")\n", + "model = pipeline.fit(data)\n", + "result = model.transform(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "K-yXeNryq5JZ", + "outputId": "51b1c852-ba1a-423e-e973-5cc6314b280f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+\n", + "| embeddings|\n", + "+--------------------+\n", + "|[-0.04292836, 0.0...|\n", + "+--------------------+\n", + "\n" + ] + } + ], + "source": [ + "result.selectExpr(\"explode(E5.embeddings) as embeddings\").show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E8l-5MELq5Ja" + }, + "source": [ + "That's it! You can now go wild and use hundreds of E5 models from HuggingFace 🤗 in Spark NLP 🚀\n" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "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" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "02a16bf5e15141389bc9f3b0451e843e": { + "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", + 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b/examples/python/transformers/openvino/HuggingFace_OpenVINO_in_Spark_NLP_LLama2.ipynb new file mode 100644 index 00000000000000..8d9e2d0fe940bf --- /dev/null +++ b/examples/python/transformers/openvino/HuggingFace_OpenVINO_in_Spark_NLP_LLama2.ipynb @@ -0,0 +1,2648 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "FvX_yCcI4W7D" + }, + "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/openvino/HuggingFace_OpenVINO_in_Spark_NLP_T5.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8J48sFcb4W7G" + }, + "source": [ + "# Import OpenVINO LLama2 models from HuggingFace 🤗 into Spark NLP 🚀\n", + "\n", + "This notebook provides a detailed walkthrough on optimizing and importing Llama2 models from HuggingFace for use in Spark NLP, with [Intel OpenVINO toolkit](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html). The focus is on converting the model to the OpenVINO format and applying precision optimizations (INT8 and INT4), to enhance the performance and efficiency on CPU platforms using [Optimum Intel](https://huggingface.co/docs/optimum/main/en/intel/inference).\n", + "\n", + "Let's keep in mind a few things before we start 😊\n", + "\n", + "- OpenVINO support was introduced in `Spark NLP 5.4.0`, enabling high performance CPU inference for models. So please make sure you have upgraded to the latest Spark NLP release.\n", + "- Model quantization is a computationally expensive process, so it is recommended to use a runtime with more than 32GB memory for exporting the quantized model from HuggingFace.\n", + "- You can import LLama models via `LlamaModel`. These models are usually under `Text Generation` category and have `Llama2` in their labels.\n", + "- Reference: [LlamaModel](https://huggingface.co/docs/transformers/model_doc/llama#transformers.LlamaModel)\n", + "- Some [example models](https://huggingface.co/models?search=Llama2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ko24PkTd4W7H" + }, + "source": [ + "## 1. Export and Save the HuggingFace model\n", + "\n", + "- Let's install `transformers` and `openvino` packages with other dependencies. You don't need `openvino` to be installed for Spark NLP, however, we need it to load and save models from HuggingFace.\n", + "- We lock `transformers` on version `4.41.2`. This doesn't mean it won't work with the future release, but we wanted you to know which versions have been tested successfully." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2rOdslOi4W7H", + "outputId": "1b0aa3f5-cbdb-423a-e7d5-1b963efd275b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/9.1 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.2/9.1 MB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/9.1 MB\u001b[0m \u001b[31m34.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m9.1/9.1 MB\u001b[0m \u001b[31m90.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.1/9.1 MB\u001b[0m \u001b[31m66.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m217.1/217.1 kB\u001b[0m \u001b[31m39.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m38.7/38.7 MB\u001b[0m \u001b[31m40.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m86.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m401.7/401.7 kB\u001b[0m \u001b[31m58.9 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[31m93.9 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[31m81.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m418.4/418.4 kB\u001b[0m \u001b[31m59.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.1/542.1 kB\u001b[0m \u001b[31m62.8 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[32m307.2/307.2 kB\u001b[0m \u001b[31m50.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.1/4.1 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[32m116.3/116.3 kB\u001b[0m \u001b[31m25.7 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[31m14.2 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[31m41.2 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[31m29.5 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[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m249.1/249.1 kB\u001b[0m \u001b[31m42.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.0/76.0 kB\u001b[0m \u001b[31m17.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.3/207.3 kB\u001b[0m \u001b[31m41.9 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[32m86.8/86.8 kB\u001b[0m \u001b[31m19.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Building wheel for jstyleson (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Building wheel for grapheme (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "\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", + "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install -q --upgrade transformers==4.41.2\n", + "!pip install -q --upgrade openvino==2024.1\n", + "!pip install -q --upgrade optimum-intel\n", + "!pip install -q --upgrade nncf\n", + "!pip install -q --upgrade huggingface_hub\n", + "!pip install -q --upgrade onnx==1.15.0\n", + "!pip install -q --upgrade torch==2.2.1" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 113, + "referenced_widgets": [ + "01440be60d4a422f8c9303b152c22628", + "34343b47023143b8ac9f9f95f956d5a4", + "cef5d2f183eb42988218fd9980c19c3b", + "4104c700f5a14ebdae8456aa2abcee91", + "5a85757ae1574d3c93c50aea6b039748", + "ae5c0a603d154b94b732498020fb276d", + "465055c1274943ef997d38f553d021bd", + "1100f06900f84b6d8e82e0d38c82c1ca", + "310ed8f7ca9c48d0abf85eef6ac60434", + "1fd4dae65e6f4456aa032799ebede6e1", + "f8f4f29ac2824d9a9c91c7c58a5e89e3", + "188424e0224b4168933b8053145b2ea4", + "0acffbe6c72e47d494ffc707c75492a5", + "f1a9b8dd0e594064b35e820d3b31d83b", + "bff72c18315e48f990aee4c3be5826a7", + "39bf68895ee6454e87bf0a7135117f87", + "9e3689e13ca8410abf8d2f03d4cc6341", + "90877bcb0ac54723a65f6f13c3702ca6", + "b30486e588604a99a5dc6aa79c3e55d2", + "d5810ec5867c4e4a8c80b1309f09dd65", + "6c4b07620b674d6c8a8a8589c9f615c9", + "20e36538f19a4ea79c2cca79da460c10", + "a3cd9e75490f48ac95547f2f54cf3d58", + "c86a57f25b564f9896e0d418f316dcd6", + "1959f8eac3714baaa9cab1d37cd3d25d", + "aa6a00579aac463f991380dfe0bbe506", + "e796cae4688d4ecca749f70343890724", + "99641947c050426ba468e8ccb765a051", + "6edbbefa17ab4fc1a12d04042cafe083" + ] + }, + "id": "bYxXi0Gr4W7J", + "outputId": "a527c3dc-abef-46fe-9609-8385877371d8" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "01440be60d4a422f8c9303b152c22628", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value='
True\n", + "/usr/local/lib/python3.10/dist-packages/optimum/exporters/openvino/model_patcher.py:438: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n", + " if sequence_length != 1:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:nncf:Statistics of the bitwidth distribution:\n", + "┍━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┑\n", + "│ Num bits (N) │ % all parameters (layers) │ % ratio-defining parameters (layers) │\n", + "┝━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┥\n", + "│ 8 │ 100% (226 / 226) │ 100% (226 / 226) │\n", + "┕━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┙\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5b522c3eeb3c4f19800ac0c459ffc608", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n"
+                        ],
+                        "text/plain": []
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "
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+                            "
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+                        ],
+                        "text/plain": [
+                            "\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Compiling the model to CPU ...\n",
+                        "Configuration saved in ./ov_models/int8/meta-llama/Llama-2-7b-chat-hf/openvino_config.json\n"
+                    ]
+                }
+            ],
+            "source": [
+                "from optimum.intel import OVModelForCausalLM\n",
+                "from transformers import LlamaTokenizer, LlamaConfig\n",
+                "\n",
+                "MODEL_NAME = \"meta-llama/Llama-2-7b-chat-hf\"\n",
+                "EXPORT_PATH = f\"./ov_models/int8/{MODEL_NAME}\"\n",
+                "\n",
+                "ov_model = OVModelForCausalLM.from_pretrained(MODEL_NAME, export=True, load_in_8bit=True)\n",
+                "tokenizer = LlamaTokenizer.from_pretrained(MODEL_NAME)\n",
+                "config = LlamaConfig.from_pretrained(MODEL_NAME)\n",
+                "\n",
+                "# Save the OpenVINO model\n",
+                "ov_model.save_pretrained(EXPORT_PATH)\n",
+                "tokenizer.save_pretrained(EXPORT_PATH)\n",
+                "config.save_pretrained(EXPORT_PATH)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "U5mMBxUEF6Z9"
+            },
+            "source": [
+                "### Option 2: Exporting to OpenVINO IR in INT4 Precision\n",
+                "\n",
+                "Alternately, Optimum Intel also provides [4-bit weight compression](https://huggingface.co/docs/optimum/intel/optimization_ov#4-bit) with `OVWeightQuantizationConfig` class to control weight quantization parameters. The `ratio` parameter controls the ratio between 4-bit and 8-bit quantization. If set to 0.8, it means that 80% of the layers will be quantized to int4 while the remaining 20% will be quantized to int8."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 4,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/",
+                    "height": 475,
+                    "referenced_widgets": [
+                        "9a504923ea28417897157cc07065fe26",
+                        "09dd4d814d1a43719a4dfd145ffe5d1d",
+                        "97aba788d25a48bb9aed50f0802d76f0",
+                        "0340296c8770497d84982352c35708ea",
+                        "830590bf17d7419c915bfd27aff3b9c3",
+                        "dccf73bd549b4e25ae1da68d0f3931fc",
+                        "a0f81e62e3f74e418fae0c9e08830f2a",
+                        "84fd5763774748eeb36f33dcb4bbe83f",
+                        "89582782ab634fd1a59994272f817d00",
+                        "13f85f42ec2941998d4e4f241e15d88a",
+                        "7cac4aa1adf84d338e7de9f3ac91bd47",
+                        "9e5afb290c1b4320a95d328c74566123",
+                        "df213f2f24d347a1aedf121c8d071345",
+                        "9b2f731950544dc1be7a5671fc1efe63",
+                        "95828bd4ddd54be4bf441952d22ae080"
+                    ]
+                },
+                "id": "n1_ZhEJ_4W7J",
+                "outputId": "6a44f056-89d5-4552-fec1-d2a843b918cd"
+            },
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'\n",
+                        "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
+                        "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+                        "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+                        "You will be able to reuse this secret in all of your notebooks.\n",
+                        "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+                        "  warnings.warn(\n",
+                        "Framework not specified. Using pt to export the model.\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "application/vnd.jupyter.widget-view+json": {
+                            "model_id": "9a504923ea28417897157cc07065fe26",
+                            "version_major": 2,
+                            "version_minor": 0
+                        },
+                        "text/plain": [
+                            "Loading checkpoint shards:   0%|          | 0/2 [00:00 True\n",
+                        "/usr/local/lib/python3.10/dist-packages/optimum/exporters/openvino/model_patcher.py:438: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
+                        "  if sequence_length != 1:\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "application/vnd.jupyter.widget-view+json": {
+                            "model_id": "9e5afb290c1b4320a95d328c74566123",
+                            "version_major": 2,
+                            "version_minor": 0
+                        },
+                        "text/plain": [
+                            "Output()"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "
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+                        ],
+                        "text/plain": []
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "
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+                            "
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+                        ],
+                        "text/plain": [
+                            "\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "INFO:nncf:Statistics of the bitwidth distribution:\n",
+                        "┍━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┑\n",
+                        "│   Num bits (N) │ % all parameters (layers)   │ % ratio-defining parameters (layers)   │\n",
+                        "┝━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┥\n",
+                        "│              8 │ 23% (58 / 226)              │ 20% (56 / 224)                         │\n",
+                        "├────────────────┼─────────────────────────────┼────────────────────────────────────────┤\n",
+                        "│              4 │ 77% (168 / 226)             │ 80% (168 / 224)                        │\n",
+                        "┕━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┙\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "application/vnd.jupyter.widget-view+json": {
+                            "model_id": "9b2f731950544dc1be7a5671fc1efe63",
+                            "version_major": 2,
+                            "version_minor": 0
+                        },
+                        "text/plain": [
+                            "Output()"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "
\n"
+                        ],
+                        "text/plain": []
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "
\n",
+                            "
\n"
+                        ],
+                        "text/plain": [
+                            "\n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Configuration saved in ./ov_models/int4/meta-llama/Llama-2-7b-chat-hf/openvino_config.json\n"
+                    ]
+                }
+            ],
+            "source": [
+                "from optimum.intel.openvino import OVWeightQuantizationConfig, OVModelForCausalLM\n",
+                "from transformers import LlamaTokenizer, LlamaConfig\n",
+                "\n",
+                "MODEL_NAME = 'meta-llama/Llama-2-7b-chat-hf'\n",
+                "EXPORT_PATH = f\"./ov_models/int4/{MODEL_NAME}\"\n",
+                "q_config = OVWeightQuantizationConfig(bits=4, sym=True, group_size=128, ratio=0.8)\n",
+                "\n",
+                "ov_model = OVModelForCausalLM.from_pretrained(MODEL_NAME, export=True, quantization_config=q_config)\n",
+                "tokenizer = LlamaTokenizer.from_pretrained(MODEL_NAME)\n",
+                "config = LlamaConfig.from_pretrained(MODEL_NAME)\n",
+                "\n",
+                "# Save the OpenVINO model\n",
+                "ov_model.save_pretrained(EXPORT_PATH)\n",
+                "tokenizer.save_pretrained(EXPORT_PATH)\n",
+                "config.save_pretrained(EXPORT_PATH)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "n4_STbc7kJji"
+            },
+            "source": [
+                "Once the model export and quantization is complete, move the model assets needed for tokenization in Spark NLP to the `assets` directory."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 5,
+            "metadata": {
+                "id": "o1mB__N1Wb4o"
+            },
+            "outputs": [],
+            "source": [
+                "!mkdir {EXPORT_PATH}/assets\n",
+                "!cp {EXPORT_PATH}/tokenizer.model {EXPORT_PATH}/assets/\n",
+                "!cp {EXPORT_PATH}/config.json {EXPORT_PATH}/assets/"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "PP6xDXDC4W7K"
+            },
+            "source": [
+                "Let's have a look inside these two directories and see what we are dealing with:"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 6,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
+                "id": "EOLmL1S14W7K",
+                "outputId": "32f9bf09-3b78-43b8-e250-9bc24aa4d4ad"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "total 4141212\n",
+                        "drwxr-xr-x 2 root root       4096 Jun  6 16:20 assets\n",
+                        "-rw-r--r-- 1 root root        732 Jun  6 16:14 config.json\n",
+                        "-rw-r--r-- 1 root root        183 Jun  6 16:14 generation_config.json\n",
+                        "-rw-r--r-- 1 root root        449 Jun  6 16:14 openvino_config.json\n",
+                        "-rw-r--r-- 1 root root 4236905793 Jun  6 16:14 openvino_model.bin\n",
+                        "-rw-r--r-- 1 root root    3159230 Jun  6 16:14 openvino_model.xml\n",
+                        "-rw-r--r-- 1 root root        414 Jun  6 16:14 special_tokens_map.json\n",
+                        "-rw-r--r-- 1 root root       1830 Jun  6 16:14 tokenizer_config.json\n",
+                        "-rw-r--r-- 1 root root     499723 Jun  6 16:14 tokenizer.model\n"
+                    ]
+                }
+            ],
+            "source": [
+                "!ls -l {EXPORT_PATH}"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 7,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
+                "id": "zQ1SbNAc4W7K",
+                "outputId": "bbb93961-3dbf-459f-d3c0-bdca7965bf53"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "total 496\n",
+                        "-rw-r--r-- 1 root root    732 Jun  6 17:32 config.json\n",
+                        "-rw-r--r-- 1 root root 499723 Jun  6 17:32 tokenizer.model\n"
+                    ]
+                }
+            ],
+            "source": [
+                "!ls -l {EXPORT_PATH}/assets"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "svbT3OG24W7L"
+            },
+            "source": [
+                "## 2. Import and Save Llama2 in Spark NLP\n",
+                "\n",
+                "- 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": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
+                "id": "z6TWf2r14W7L",
+                "outputId": "80adffa3-73a6-46f0-87ca-3edc91c149e9"
+            },
+            "outputs": [],
+            "source": [
+                "! wget -q http://setup.johnsnowlabs.com/colab.sh -O - | bash"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "OYI03iqp4W7L"
+            },
+            "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": "7_Oy0zMi4W7L",
+                "outputId": "02179885-e49e-4b97-d9a6-d5ac6fb5991a"
+            },
+            "outputs": [],
+            "source": [
+                "import sparknlp\n",
+                "\n",
+                "# let's start Spark with Spark NLP\n",
+                "spark = sparknlp.start()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "aXCJqb9i4W7M"
+            },
+            "source": [
+                "- Let's use `loadSavedModel` functon in `LLAMA2Transformer` which allows us to load the OpenVINO model.\n",
+                "- Most params will be set automatically. They can also be set later after loading the model in `LLAMA2Transformer` during runtime, so don't worry about setting them now.\n",
+                "- `loadSavedModel` accepts two params, first is the path to the exported model. 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.st 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": 10,
+            "metadata": {
+                "id": "T3591W9R4W7M"
+            },
+            "outputs": [],
+            "source": [
+                "from sparknlp.annotator import *\n",
+                "\n",
+                "llama2 = LLAMA2Transformer \\\n",
+                "    .loadSavedModel(EXPORT_PATH, spark) \\\n",
+                "    .setMaxOutputLength(50) \\\n",
+                "    .setDoSample(False) \\\n",
+                "    .setTopK(50) \\\n",
+                "    .setInputCols([\"documents\"]) \\\n",
+                "    .setOutputCol(\"generation\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "9X3RphM-4W7M"
+            },
+            "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": 11,
+            "metadata": {
+                "id": "T6GaugQa4W7M"
+            },
+            "outputs": [],
+            "source": [
+                "llama2.write().overwrite().save(f\"{MODEL_NAME}_spark_nlp\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "o0kroa6u4W7M"
+            },
+            "source": [
+                "Let's clean up stuff we don't need anymore"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 12,
+            "metadata": {
+                "id": "BHvWriCn4W7M"
+            },
+            "outputs": [],
+            "source": [
+                "!rm -rf {EXPORT_PATH}"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "Gz4cU4Q54W7N"
+            },
+            "source": [
+                "Awesome  😎 !\n",
+                "\n",
+                "This is your OpenVINO LLama2 model from HuggingFace 🤗  loaded and saved by Spark NLP 🚀"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 13,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
+                "id": "17klLp1M4W7N",
+                "outputId": "eccfaaba-5b98-4914-dcfc-aedb8de3d285"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "total 4141828\n",
+                        "drwxr-xr-x 3 root root       4096 Jun  6 16:35 fields\n",
+                        "-rw-r--r-- 1 root root 4240712291 Jun  6 16:36 llama2_openvino\n",
+                        "-rw-r--r-- 1 root root     499723 Jun  6 16:36 llama2_spp\n",
+                        "drwxr-xr-x 2 root root       4096 Jun  6 16:35 metadata\n"
+                    ]
+                }
+            ],
+            "source": [
+                "! ls -l {MODEL_NAME}_spark_nlp"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "3R_rS8Fj4W7N"
+            },
+            "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 Llama2 model 😊"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 14,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
+                "id": "uxSo5-b24W7N",
+                "outputId": "c4c91a3a-de46-41d7-98c7-e301fbe9419a"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
+                        "|result                                                                                                                                                                                                                                                                                                                          |\n",
+                        "+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
+                        "|[Llama 2 outperforms other open language models on many external benchmarks, including GLUE, SuperGLUE, and LAMA. Unterscheidung between the two models is not straightforward, and the authors propose several possible explanations for the observed differences.\\n\\nOne possible explanation is that the Llama 2 model has a]|\n",
+                        "+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
+                        "\n"
+                    ]
+                }
+            ],
+            "source": [
+                "import sparknlp\n",
+                "from sparknlp.base import *\n",
+                "from sparknlp.annotator import *\n",
+                "from pyspark.ml import Pipeline\n",
+                "\n",
+                "test_data = spark.createDataFrame([\n",
+                "    [\"Llama 2 outperforms other open language models on many external benchmarks,\"]\n",
+                "]).toDF(\"text\")\n",
+                "\n",
+                "\n",
+                "document_assembler = DocumentAssembler() \\\n",
+                "    .setInputCol(\"text\")\\\n",
+                "    .setOutputCol(\"document\")\n",
+                "\n",
+                "llama2 = LLAMA2Transformer.load(f\"{MODEL_NAME}_spark_nlp\") \\\n",
+                "  .setInputCols([\"document\"]) \\\n",
+                "  .setOutputCol(\"generation\")\n",
+                "\n",
+                "pipeline = Pipeline().setStages([document_assembler, llama2])\n",
+                "\n",
+                "result = pipeline.fit(test_data).transform(test_data)\n",
+                "result.select(\"generation.result\").show(truncate=False)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "PdvQAAfo4W7N"
+            },
+            "source": [
+                "That's it! You can now go wild and use hundreds of Llama2 models from HuggingFace 🤗 in Spark NLP 🚀\n"
+            ]
+        }
+    ],
+    "metadata": {
+        "colab": {
+            "provenance": [
+                {
+                    "file_id": "1zEVIT5iT2YGNBRBTHoqvWyXK4_GgInlb",
+                    "timestamp": 1717627535020
+                }
+            ]
+        },
+        "kernelspec": {
+            "display_name": "Python 3",
+            "name": "python3"
+        },
+        "language_info": {
+            "name": "python"
+        },
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diff --git a/examples/python/transformers/openvino/HuggingFace_OpenVINO_in_Spark_NLP_RoBERTa.ipynb b/examples/python/transformers/openvino/HuggingFace_OpenVINO_in_Spark_NLP_RoBERTa.ipynb
new file mode 100644
index 00000000000000..438a4a8521bbaf
--- /dev/null
+++ b/examples/python/transformers/openvino/HuggingFace_OpenVINO_in_Spark_NLP_RoBERTa.ipynb
@@ -0,0 +1,2754 @@
+{
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "wVAlZxT4kyVZ"
+            },
+            "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/openvino/HuggingFace_OpenVINO_in_Spark_NLP_RoBERTa.ipynb)\n",
+                "\n",
+                "# Import OpenVINO RoBERTa models from HuggingFace 🤗 into Spark NLP 🚀\n",
+                "\n",
+                "This notebook provides a detailed walkthrough on optimizing and exporting RoBerta models from HuggingFace for use in Spark NLP, leveraging the various tools provided in the [Intel OpenVINO toolkit](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html) ecosystem.\n",
+                "\n",
+                "Let's keep in mind a few things before we start 😊\n",
+                "\n",
+                "- OpenVINO support was introduced in  `Spark NLP 5.4.0`, enabling high performance inference for models. Please make sure you have upgraded to the latest Spark NLP release.\n",
+                "- You can import models for RoBERTa from HuggingFace and they have to be in `Fill Mask` category. Meaning, you cannot use RoBERTa models trained/fine-tuned on a specific task such as token/sequence classification."
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "wwk95X-XkyVc"
+            },
+            "source": [
+                "## 1. Export and Save HuggingFace model"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "Ok7Vdy7_kyVd"
+            },
+            "source": [
+                "- Let's install `transformers` and `openvino` packages with other dependencies. You don't need `openvino` to be installed for Spark NLP, however, we need it to load and save models from HuggingFace.\n",
+                "- We lock `transformers` on version `4.41.2`. This doesn't mean it won't work with the future releases, but we wanted you to know which versions have been tested successfully."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 1,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
+                "id": "sOkBNCFckyVd",
+                "outputId": "2032c35c-7105-424d-c515-89349993f679"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m38.7/38.7 MB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+                        "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m217.1/217.1 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+                        "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m418.4/418.4 kB\u001b[0m \u001b[31m11.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+                        "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.1/542.1 kB\u001b[0m \u001b[31m14.1 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[31m56.0 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[31m12.1 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[31m6.1 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[31m19.2 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[31m14.5 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[31m4.7 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[31m36.9 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[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+                        "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+                        "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\u001b[0m\u001b[31m\n",
+                        "\u001b[0m"
+                    ]
+                }
+            ],
+            "source": [
+                "!pip install -q --upgrade transformers==4.41.2\n",
+                "!pip install -q --upgrade openvino==2024.1\n",
+                "!pip install -q --upgrade optimum-intel"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "NI2ytaUXkyVe"
+            },
+            "source": [
+                "[Optimum Intel](https://github.com/huggingface/optimum-intel?tab=readme-ov-file#openvino) is the interface between the Transformers library and the various model optimization and acceleration tools provided by Intel. HuggingFace models loaded with optimum-intel are automatically optimized for OpenVINO, while being compatible with the Transformers API.\n",
+                "- To load a HuggingFace model directly for inference/export, just replace the `AutoModelForXxx` class with the corresponding `OVModelForXxx` class. We can use this to import and export OpenVINO models with `from_pretrained` and `save_pretrained`.\n",
+                "- By setting `export=True`, the source model is converted to OpenVINO IR format on the fly.\n",
+                "- We'll use [roberta-base](https://huggingface.co/roberta-base) model from HuggingFace as an example and load it as a `OVModelForFeatureExtraction`, representing an OpenVINO model.\n",
+                "- In addition to the RoBERTa model, we also need to save the tokenizer. This is the same for every model, these are assets (saved in `/assets`) needed for tokenization inside Spark NLP."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 2,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/",
+                    "height": 552,
+                    "referenced_widgets": [
+                        "0b583a36b22d4c9a8dca169d629c4a6f",
+                        "af4f3de1414e49e48a07eeebffcece68",
+                        "89957ac069944ce6b8b3ef1ac7c049b0",
+                        "e093f1cae9284dd8943fa0c897f7a166",
+                        "1c87decf783346889cfd6e1f4049eb65",
+                        "908b229d0b0044ca9bc6489c1f7b060d",
+                        "ca9fd9279f154f089b66299845b0dca4",
+                        "8c6b5991638147fba7dca85a456a2fe3",
+                        "3304b5813ba64aa6b7ad745c6c85b415",
+                        "29d0da447d9e4af6bdd2aebd7cc3a44e",
+                        "8e8ee2fe7e864bfe9b3d213d5dfc851e",
+                        "586487669aa04d9e8d6d87b766ce3451",
+                        "96e2c57dd7b048aea8a8f7cab5213196",
+                        "c21b6c9eba654327a291ac936cccd89c",
+                        "e361fba88809470e9427e02d6505281c",
+                        "be685640f19a4584a020d0dda7ac0d73",
+                        "1bab31c3b07847d7aa6b76691b5df71d",
+                        "199c8e081ba2412c98190dc43e154c28",
+                        "95dc2cbf0c7f48ffbfc0a2a8c8a73e84",
+                        "b8503187e8b541be92f745db40f278bd",
+                        "2c2b7a75f3314c53b0f5f51a653c0aee",
+                        "2478cbf7407746f49105ebd3fefe6e5f",
+                        "23a0f1784c4246ed9a5560507a869539",
+                        "d9b82248c9bb49b699c7a69ff8a02554",
+                        "b1e1e5451aaa42b1a98a85b15d374902",
+                        "2203bf7a3ed3417b9700a2fc08ba6f95",
+                        "f2846de93f824dc9a470017ec726982f",
+                        "165b8a0547884955a56f7b3c5be5876a",
+                        "3437b38f18ec4542b19786686be5cd1c",
+                        "aa459a90c8d74a2eaf078c17f02b1d68",
+                        "d59e7e8777dd4be9adc8863b67cd6f78",
+                        "c3e8b92736ed400cb5bec1906cce343f",
+                        "5d7fda582df84faf846a1c64fd924af4",
+                        "4f966aee308f4453bd629a0068673575",
+                        "ae8009add3dd4097a643701cdc1a2a26",
+                        "bb1f526fefc249b5851ea495b8081c31",
+                        "0fb9d1c549cf40259ab88548d14873f2",
+                        "3b1879d55afe4125bb2fd270223b7ac7",
+                        "a7fb488b43f94e7f8e423c95175c1fc4",
+                        "cc7c8b7126f141439992b38e412aa460",
+                        "849a89fbb36a40aca5d8edfb7f0fc4ef",
+                        "4d3cc5a936ac49438e112b814829b046",
+                        "d3fdabb3d878420c80a96e4fc39f3f17",
+                        "5b1f0c6f685248f7b3c2ddab106a63ba",
+                        "fd39639561dc40ca8725428b59ec15c2",
+                        "a425de7e88564f609d6087cfed438d23",
+                        "d56ffc692ccb496ca650ae98b8d33891",
+                        "c906ed0beb6042639aecc8c9d79357c7",
+                        "b77d0e06b3ac476bbf77f7f72ed3b68f",
+                        "4e3d347165fd471b8d67f0a3ad1fea2b",
+                        "88bc2e60824c425db0ecbd97164f1993",
+                        "a7b61e106ae943b3b56cea5457b6b184",
+                        "60e453a88b744b9293810ab54b82b4f6",
+                        "14c8bae12fb54882aa0b0d3dcdd6b029",
+                        "17ee157c13204326b96c7a39aacd86c6",
+                        "20ea0ef398f0437c8d4f4fd32b8850ea",
+                        "14178cf1f2c34d598ba93ad0491af77a",
+                        "54bfb46710bb44bc9459dc85683a932e",
+                        "8d377302f4ef4b959963a54e02e37551",
+                        "fbb3fcb8c2674edcb8206e891a899c97",
+                        "641abeb4ee4a4df88f43952d63ada76e",
+                        "8867bb2053d342a3ad06480445636b08",
+                        "95f4948f8ea44de0b4ac283e56ffc5b1",
+                        "0fcdedf1dba643deab4e02792eed20a8",
+                        "04261ea1c52942b5a7346b847dc34ef5",
+                        "16a0f59f1743493cb7d4face3c59dcf3"
+                    ]
+                },
+                "id": "lilDAJaWkyVf",
+                "outputId": "62642dce-dba9-452f-cefa-65a17d658266"
+            },
+            "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": "0b583a36b22d4c9a8dca169d629c4a6f",
+                            "version_major": 2,
+                            "version_minor": 0
+                        },
+                        "text/plain": [
+                            "config.json:   0%|          | 0.00/481 [00:00 False\n",
+                        "Compiling the model to CPU ...\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "text/plain": [
+                            "('ov_models/roberta-base/tokenizer_config.json',\n",
+                            " 'ov_models/roberta-base/special_tokens_map.json',\n",
+                            " 'ov_models/roberta-base/vocab.json',\n",
+                            " 'ov_models/roberta-base/merges.txt',\n",
+                            " 'ov_models/roberta-base/added_tokens.json',\n",
+                            " 'ov_models/roberta-base/tokenizer.json')"
+                        ]
+                    },
+                    "execution_count": 2,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "from optimum.intel import OVModelForFeatureExtraction\n",
+                "from transformers import AutoTokenizer\n",
+                "\n",
+                "MODEL_NAME = \"roberta-base\"\n",
+                "EXPORT_PATH = f\"ov_models/{MODEL_NAME}\"\n",
+                "\n",
+                "ov_model = OVModelForFeatureExtraction.from_pretrained(MODEL_NAME, export=True)\n",
+                "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
+                "\n",
+                "# Save the OpenVINO model\n",
+                "ov_model.save_pretrained(EXPORT_PATH)\n",
+                "tokenizer.save_pretrained(EXPORT_PATH)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 3,
+            "metadata": {
+                "id": "gLvTb3InkyVf"
+            },
+            "outputs": [],
+            "source": [
+                "# Create directory for assets\n",
+                "!mkdir {EXPORT_PATH}/assets\n",
+                "\n",
+                "# let's make sure we sort the vocabs based on their ids first\n",
+                "vocabs = tokenizer.get_vocab()\n",
+                "vocabs = sorted(vocabs, key=vocabs.get)\n",
+                "\n",
+                "# let's save the vocab as txt file\n",
+                "with open(f'{EXPORT_PATH}/vocab.txt', 'w') as f:\n",
+                "    for item in vocabs:\n",
+                "        f.write(\"%s\\n\" % item)\n",
+                "\n",
+                "# let's copy both vocab.txt and merges.txt files to /assets directory\n",
+                "!cp {EXPORT_PATH}/vocab.txt {EXPORT_PATH}/assets\n",
+                "!cp {EXPORT_PATH}/merges.txt {EXPORT_PATH}/assets"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "AoSTUezAkyVg"
+            },
+            "source": [
+                "Let's have a look inside these two directories and see what we are dealing with:"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 4,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
+                "id": "UkdPiEZAkyVh",
+                "outputId": "c8ea598d-3cd7-4f96-8a8d-80f6a476cd54"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "total 488692\n",
+                        "drwxr-xr-x 2 root root      4096 Jun  6 18:29 assets\n",
+                        "-rw-r--r-- 1 root root       644 Jun  6 18:27 config.json\n",
+                        "-rw-r--r-- 1 root root    456318 Jun  6 18:27 merges.txt\n",
+                        "-rw-r--r-- 1 root root 496224444 Jun  6 18:27 openvino_model.bin\n",
+                        "-rw-r--r-- 1 root root    400929 Jun  6 18:27 openvino_model.xml\n",
+                        "-rw-r--r-- 1 root root       280 Jun  6 18:27 special_tokens_map.json\n",
+                        "-rw-r--r-- 1 root root      1215 Jun  6 18:27 tokenizer_config.json\n",
+                        "-rw-r--r-- 1 root root   2108643 Jun  6 18:27 tokenizer.json\n",
+                        "-rw-r--r-- 1 root root    798293 Jun  6 18:27 vocab.json\n",
+                        "-rw-r--r-- 1 root root    407065 Jun  6 18:29 vocab.txt\n"
+                    ]
+                }
+            ],
+            "source": [
+                "!ls -l {EXPORT_PATH}"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 5,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
+                "id": "V93BPbnOkyVh",
+                "outputId": "748dba63-89a9-4296-f9dd-e0c15aa83eed"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "total 848\n",
+                        "-rw-r--r-- 1 root root 456318 Jun  6 18:29 merges.txt\n",
+                        "-rw-r--r-- 1 root root 407065 Jun  6 18:29 vocab.txt\n"
+                    ]
+                }
+            ],
+            "source": [
+                "!ls -l {EXPORT_PATH}/assets"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "wMkTaUXnkyVh"
+            },
+            "source": [
+                "## 2. Import and Save RoBERTa in Spark NLP\n",
+                "\n",
+                "- 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": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
+                "id": "nBELsKiWkyVi",
+                "outputId": "fe9d4742-314f-4666-c7f0-2c24658214b2"
+            },
+            "outputs": [],
+            "source": [
+                "! wget -q http://setup.johnsnowlabs.com/colab.sh -O - | bash"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "N8K2kUeGkyVi"
+            },
+            "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": "I7U1YwcUkyVi",
+                "outputId": "2a39c65b-a3b9-4ce1-afca-7672a5328a9f"
+            },
+            "outputs": [],
+            "source": [
+                "import sparknlp\n",
+                "# let's start Spark with Spark NLP\n",
+                "spark = sparknlp.start()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "eB5AXJwHkyVj"
+            },
+            "source": [
+                "- Let's use `loadSavedModel` functon in `RoBertaEmbeddings` which allows us to load the OpenVINO model.\n",
+                "- Most params will be set automatically. They can also be set later after loading the model in `RoBertaEmbeddings` during runtime, so don't worry about setting them now.\n",
+                "- `loadSavedModel` accepts two params, first is the path to the exported model. The second is the SparkSession that is `spark` variable we previously started via `sparknlp.start()`\n",
+                "- `setStorageRef` is very important. When you are training a task like NER or any Text Classification, we use this reference to bound the trained model to this specific embeddings so you won't load a different embeddings by mistake and see terrible results. 😊\n",
+                "- It's up to you what you put in `setStorageRef` but it cannot be changed later on. We usually use the name of the model to be clear, but you can get creative if you want!\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.st 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"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 8,
+            "metadata": {
+                "id": "1B83tQtAkyVj"
+            },
+            "outputs": [],
+            "source": [
+                "from sparknlp.annotator import *\n",
+                "\n",
+                "# All these params should be identical to the original model\n",
+                "roberta = RoBertaEmbeddings.loadSavedModel(f\"{EXPORT_PATH}\", spark)\\\n",
+                "    .setInputCols([\"document\",'token'])\\\n",
+                "    .setOutputCol(\"roberta\")\\\n",
+                "    .setCaseSensitive(True)\\\n",
+                "    .setStorageRef('roberta-base')"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "Y4ipccMHkyVk"
+            },
+            "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": 9,
+            "metadata": {
+                "id": "jiTgV72mkyVk"
+            },
+            "outputs": [],
+            "source": [
+                "roberta.write().overwrite().save(f\"{MODEL_NAME}_spark_nlp\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "HBaDgFkTkyVk"
+            },
+            "source": [
+                "Let's clean up stuff we don't need anymore"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 10,
+            "metadata": {
+                "id": "M4n3_6h1kyVk"
+            },
+            "outputs": [],
+            "source": [
+                "!rm -rf {EXPORT_PATH}"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "xrakj_JKkyVl"
+            },
+            "source": [
+                "Awesome  😎 !\n",
+                "\n",
+                "This is your OpenVINO RoBERTa model from HuggingFace 🤗  loaded and saved by Spark NLP 🚀"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 11,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
+                "id": "E1EsiWbSkyVm",
+                "outputId": "ade0235e-82ea-4595-ab5e-1e76b54ea1d7"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "total 485068\n",
+                        "drwxr-xr-x 4 root root      4096 Jun  6 18:32 fields\n",
+                        "drwxr-xr-x 2 root root      4096 Jun  6 18:32 metadata\n",
+                        "-rw-r--r-- 1 root root 496701436 Jun  6 18:32 roberta_openvino\n"
+                    ]
+                }
+            ],
+            "source": [
+                "! ls -l {MODEL_NAME}_spark_nlp"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "TpqJXnW7kyVm"
+            },
+            "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 RoBERTa model 😊"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 12,
+            "metadata": {
+                "id": "oLpo6d7okyVm"
+            },
+            "outputs": [],
+            "source": [
+                "import sparknlp\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",
+                "roberta_loaded = RoBertaEmbeddings.load(f\"{MODEL_NAME}_spark_nlp\")\\\n",
+                "    .setInputCols([\"document\",'token'])\\\n",
+                "    .setOutputCol(\"roberta\")\\\n",
+                "\n",
+                "pipeline = Pipeline(\n",
+                "    stages = [\n",
+                "        document_assembler,\n",
+                "        tokenizer,\n",
+                "        roberta_loaded\n",
+                "  ])\n",
+                "\n",
+                "data = spark.createDataFrame([['William Henry Gates III (born October 28, 1955) is an American business magnate, software developer, investor,and philanthropist.']]).toDF(\"text\")\n",
+                "model = pipeline.fit(data)\n",
+                "result = model.transform(data)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 13,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
+                "id": "jdEv9RdWkyVm",
+                "outputId": "aea131e8-2a8c-4189-cec1-2ce46a4fa77e"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "+--------------------+\n",
+                        "|          embeddings|\n",
+                        "+--------------------+\n",
+                        "|[0.05307511, 0.10...|\n",
+                        "|[0.008972348, 0.0...|\n",
+                        "|[-0.013229029, 0....|\n",
+                        "|[-3.336197E-4, -0...|\n",
+                        "|[-0.16356963, -0....|\n",
+                        "|[-0.20351873, -0....|\n",
+                        "|[-0.0888931, -0.1...|\n",
+                        "|[-0.19946289, 0.0...|\n",
+                        "|[0.025318686, -0....|\n",
+                        "|[0.0024142172, -0...|\n",
+                        "|[0.14282271, -0.4...|\n",
+                        "|[0.22885321, 0.02...|\n",
+                        "|[0.1516986, 0.156...|\n",
+                        "|[-0.031728476, 0....|\n",
+                        "|[0.060404696, 0.1...|\n",
+                        "|[-0.044417936, 0....|\n",
+                        "|[0.20480454, -0.4...|\n",
+                        "|[0.177825, -0.016...|\n",
+                        "|[0.051053435, -0....|\n",
+                        "|[0.15343398, -0.2...|\n",
+                        "+--------------------+\n",
+                        "only showing top 20 rows\n",
+                        "\n"
+                    ]
+                }
+            ],
+            "source": [
+                "result.selectExpr(\"explode(roberta.embeddings) as embeddings\").show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "57MdRjgHkyVm"
+            },
+            "source": [
+                "That's it! You can now go wild and use hundreds of RoBERTa models from HuggingFace 🤗 in Spark NLP 🚀\n"
+            ]
+        }
+    ],
+    "metadata": {
+        "colab": {
+            "provenance": []
+        },
+        "kernelspec": {
+            "display_name": "Python 3",
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diff --git a/examples/python/transformers/openvino/HuggingFace_OpenVINO_in_Spark_NLP_T5.ipynb b/examples/python/transformers/openvino/HuggingFace_OpenVINO_in_Spark_NLP_T5.ipynb
new file mode 100644
index 00000000000000..b3f71bc61176e1
--- /dev/null
+++ b/examples/python/transformers/openvino/HuggingFace_OpenVINO_in_Spark_NLP_T5.ipynb
@@ -0,0 +1,3085 @@
+{
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "FvX_yCcI4W7D"
+            },
+            "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/openvino/HuggingFace_OpenVINO_in_Spark_NLP_T5.ipynb)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "8J48sFcb4W7G"
+            },
+            "source": [
+                "# Import OpenVINO T5 models from HuggingFace 🤗 into Spark NLP 🚀\n",
+                "\n",
+                "This notebook provides a detailed walkthrough on optimizing and exporting T5 models from HuggingFace for use in Spark NLP, leveraging the various tools provided in the [Intel OpenVINO toolkit](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html) ecosystem.\n",
+                "\n",
+                "Let's keep in mind a few things before we start 😊\n",
+                "\n",
+                "- OpenVINO support was introduced in  `Spark NLP 5.4.0`, enabling high performance inference for models. So please make sure you have upgraded to the latest Spark NLP release.\n",
+                "- You can import T5 models via `T5Model`. These models are usually under `Text2Text Generation` category and have `T5` in their labels.\n",
+                "- Reference: [T5Model](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Model)\n",
+                "- Some [example models](https://huggingface.co/models?other=T5)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "Ko24PkTd4W7H"
+            },
+            "source": [
+                "## 1. Export and Save HuggingFace model\n",
+                "\n",
+                "- Let's install `transformers` and `openvino` packages with other dependencies. You don't need `openvino` to be installed for Spark NLP, however, we need it to load and save models from HuggingFace.\n",
+                "- We lock `transformers` on version `4.41.2`. This doesn't mean it won't work with the future releases\n",
+                "- We will also need `sentencepiece` for tokenization."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
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+                "outputId": "871c9ec8-93b7-4445-cb39-5a5e5f634c7b"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m217.1/217.1 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+                        "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m38.7/38.7 MB\u001b[0m \u001b[31m12.8 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[31m18.8 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[31m22.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+                        "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m755.5/755.5 MB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+                        "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m166.0/166.0 MB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+                        "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m167.9/167.9 MB\u001b[0m \u001b[31m6.4 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[31m30.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+                        "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m418.4/418.4 kB\u001b[0m \u001b[31m32.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+                        "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.1/542.1 kB\u001b[0m \u001b[31m39.3 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[31m12.5 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[31m7.4 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[31m21.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+                        "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m15.2 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[31m3.7 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[31m10.8 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",
+                        "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
+                        "torchaudio 2.3.0+cu121 requires torch==2.3.0, but you have torch 2.2.1 which is incompatible.\n",
+                        "torchtext 0.18.0 requires torch>=2.3.0, but you have torch 2.2.1 which is incompatible.\n",
+                        "torchvision 0.18.0+cu121 requires torch==2.3.0, but you have torch 2.2.1 which is incompatible.\u001b[0m\u001b[31m\n",
+                        "\u001b[0m"
+                    ]
+                }
+            ],
+            "source": [
+                "!pip install -q --upgrade transformers==4.41.2 optimum-intel openvino==2024.1 sentencepiece onnx==1.15.0 torch==2.2.1"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "ix0eFHLu4W7J"
+            },
+            "source": [
+                "[Optimum Intel](https://github.com/huggingface/optimum-intel?tab=readme-ov-file#openvino) is the interface between the Transformers library and the various model optimization and acceleration tools provided by Intel. HuggingFace models loaded with optimum-intel are automatically optimized for OpenVINO, while being compatible with the Transformers API.\n",
+                "- To load a HuggingFace model directly for inference/export, just replace the `AutoModelForXxx` class with the corresponding `OVModelForXxx` class. We can use this to import and export OpenVINO models with `from_pretrained` and `save_pretrained`.\n",
+                "- By setting `export=True`, the source model is converted to OpenVINO IR format on the fly.\n",
+                "- We'll use [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) model from HuggingFace as an example\n",
+                "- In addition to `T5Model` we also need to save the tokenizer. This is the same for every model, these are assets needed for tokenization inside Spark NLP."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "id": "bYxXi0Gr4W7J"
+            },
+            "outputs": [],
+            "source": [
+                "# Model name, either HF (e.g. \"google/flan-t5-base\") or a local path\n",
+                "MODEL_NAME = \"google/flan-t5-base\"\n",
+                "\n",
+                "\n",
+                "# Path to store the exported models\n",
+                "EXPORT_PATH = f\"ov_models/{MODEL_NAME}\""
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/",
+                    "height": 686,
+                    "referenced_widgets": [
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+                        "8ac1ee3ad59d4013b5851786be801704",
+                        "591f316a029942419e1e4618877e1451",
+                        "23e3c42d17564fe3993d996fd3dbc01f",
+                        "a6d481427ff748e8b8269f1c8397960a",
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+                        "28291470ecab4a2da0e13e243656e339",
+                        "ab9be6ae5d474084aba66e5acdeb5649",
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+                        "f04aff55acad4696bc5fb570d253b37a",
+                        "efa36ae78fba44109e0333d76d59c45f",
+                        "abc033dda30d420faf1f7574990c5b76",
+                        "882359df57674d35a92639bcb8d6e88c",
+                        "7614f618e5ae4e40be3b69cbc488ed3e",
+                        "4789637191b24344ad5eee75ba3f04f1",
+                        "94a513fa4ac74eecb2f6edb3ff9b5962",
+                        "018d865db1454551bc22ae9541971dc9",
+                        "d3d03a00e705437b9a0b25244498d99f",
+                        "9d8749e2b02240538856d7423b71ef77",
+                        "22d69ec11abb4cfb8197db8049829b02",
+                        "76df60c902334ae5b1cf5db50f7ea804",
+                        "5f37c86cb27b45aeb042439bdd492c17",
+                        "7a4799af20a448b593a98db59a35d3f8",
+                        "cfc4820483934e51b77adef69dda3a41",
+                        "1e011ed9ac8b4ca29ca16920eef61084"
+                    ]
+                },
+                "id": "n1_ZhEJ_4W7J",
+                "outputId": "ceea1264-98f1-4da8-d974-529d0827a0d6"
+            },
+            "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": "0c9390a198ca4ab7ac9e772dd9a06a3a",
+                            "version_major": 2,
+                            "version_minor": 0
+                        },
+                        "text/plain": [
+                            "config.json:   0%|          | 0.00/1.40k [00:00 False\n",
+                        "Using framework PyTorch: 2.2.1+cu121\n",
+                        "Overriding 1 configuration item(s)\n",
+                        "\t- use_cache -> True\n",
+                        "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:1018: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
+                        "  if causal_mask.shape[1] < attention_mask.shape[1]:\n",
+                        "Using framework PyTorch: 2.2.1+cu121\n",
+                        "Overriding 1 configuration item(s)\n",
+                        "\t- use_cache -> True\n",
+                        "/usr/local/lib/python3.10/dist-packages/transformers/models/t5/modeling_t5.py:501: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
+                        "  elif past_key_value.shape[2] != key_value_states.shape[1]:\n",
+                        "Compiling the encoder to CPU ...\n",
+                        "Compiling the decoder to CPU ...\n",
+                        "Compiling the decoder to CPU ...\n",
+                        "You are using the default legacy behaviour of the . This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n",
+                        "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
+                    ]
+                }
+            ],
+            "source": [
+                "from optimum.intel import OVModelForSeq2SeqLM\n",
+                "from transformers import T5Tokenizer\n",
+                "\n",
+                "ov_model = OVModelForSeq2SeqLM.from_pretrained(MODEL_NAME, export=True)\n",
+                "tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME)\n",
+                "\n",
+                "# Save the OpenVINO model\n",
+                "ov_model.save_pretrained(EXPORT_PATH)\n",
+                "tokenizer.save_pretrained(EXPORT_PATH)\n",
+                "\n",
+                "# Create directory for assets and move the tokenizer files.\n",
+                "# A separate folder is needed for Spark NLP.\n",
+                "! mkdir -p {EXPORT_PATH}/assets\n",
+                "! mv -t {EXPORT_PATH}/assets {EXPORT_PATH}/spiece.model"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "PP6xDXDC4W7K"
+            },
+            "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": "EOLmL1S14W7K",
+                "outputId": "46cfdfef-9a40-4c0d-ac02-d02f568e7417"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "total 1866848\n",
+                        "-rw-r--r-- 1 root root      2593 Jun  6 18:41 added_tokens.json\n",
+                        "drwxr-xr-x 2 root root      4096 Jun  6 18:41 assets\n",
+                        "-rw-r--r-- 1 root root      1529 Jun  6 18:41 config.json\n",
+                        "-rw-r--r-- 1 root root       142 Jun  6 18:41 generation_config.json\n",
+                        "-rw-r--r-- 1 root root 725992092 Jun  6 18:41 openvino_decoder_model.bin\n",
+                        "-rw-r--r-- 1 root root    850479 Jun  6 18:41 openvino_decoder_model.xml\n",
+                        "-rw-r--r-- 1 root root 669369060 Jun  6 18:41 openvino_decoder_with_past_model.bin\n",
+                        "-rw-r--r-- 1 root root    830208 Jun  6 18:41 openvino_decoder_with_past_model.xml\n",
+                        "-rw-r--r-- 1 root root 514011828 Jun  6 18:41 openvino_encoder_model.bin\n",
+                        "-rw-r--r-- 1 root root    530439 Jun  6 18:41 openvino_encoder_model.xml\n",
+                        "-rw-r--r-- 1 root root      2543 Jun  6 18:41 special_tokens_map.json\n",
+                        "-rw-r--r-- 1 root root     20817 Jun  6 18:41 tokenizer_config.json\n"
+                    ]
+                }
+            ],
+            "source": [
+                "!ls -l {EXPORT_PATH}"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
+                "id": "zQ1SbNAc4W7K",
+                "outputId": "9c2bc41d-4145-4e6a-9d5a-fbd857dbe500"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "total 776\n",
+                        "-rw-r--r-- 1 root root 791656 Jun  6 18:41 spiece.model\n"
+                    ]
+                }
+            ],
+            "source": [
+                "!ls -l {EXPORT_PATH}/assets"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "svbT3OG24W7L"
+            },
+            "source": [
+                "## 2. Import and Save T5 in Spark NLP\n",
+                "\n",
+                "- 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": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
+                "id": "z6TWf2r14W7L",
+                "outputId": "3a9a946a-e451-49a4-d78a-ec0d0a356b05"
+            },
+            "outputs": [],
+            "source": [
+                "! wget -q http://setup.johnsnowlabs.com/colab.sh -O - | bash"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "OYI03iqp4W7L"
+            },
+            "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": "7_Oy0zMi4W7L",
+                "outputId": "6cff2336-f564-443c-9f12-fe6160d60d8c"
+            },
+            "outputs": [],
+            "source": [
+                "import sparknlp\n",
+                "\n",
+                "# let's start Spark with Spark NLP\n",
+                "spark = sparknlp.start()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "aXCJqb9i4W7M"
+            },
+            "source": [
+                "- Let's use `loadSavedModel` functon in `T5Transformer` which allows us to load the OpenVINO model.\n",
+                "- Most params will be set automatically. They can also be set later after loading the model in `T5Transformer` during runtime, so don't worry about setting them now.\n",
+                "- `loadSavedModel` accepts two params, first is the path to the exported model. 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.st 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": "T3591W9R4W7M"
+            },
+            "outputs": [],
+            "source": [
+                "from sparknlp.annotator import *\n",
+                "\n",
+                "T5 = T5Transformer.loadSavedModel(EXPORT_PATH, spark)\\\n",
+                "  .setTask(\"summarize:\") \\\n",
+                "  .setMaxOutputLength(200)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "9X3RphM-4W7M"
+            },
+            "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": "T6GaugQa4W7M"
+            },
+            "outputs": [],
+            "source": [
+                "T5.write().overwrite().save(f\"{MODEL_NAME}_spark_nlp\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "o0kroa6u4W7M"
+            },
+            "source": [
+                "Let's clean up stuff we don't need anymore"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "id": "BHvWriCn4W7M"
+            },
+            "outputs": [],
+            "source": [
+                "!rm -rf {EXPORT_PATH}"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "Gz4cU4Q54W7N"
+            },
+            "source": [
+                "Awesome  😎 !\n",
+                "\n",
+                "This is your OpenVINO T5 model from HuggingFace 🤗  loaded and saved by Spark NLP 🚀"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
+                "id": "17klLp1M4W7N",
+                "outputId": "dbad8494-0dbf-4ed0-8442-8279062bece1"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "total 1867860\n",
+                        "-rw-r--r-- 1 root root 726953791 Jun  6 19:08 decoder\n",
+                        "-rw-r--r-- 1 root root 670301888 Jun  6 19:09 decoder_with_past\n",
+                        "-rw-r--r-- 1 root root 514621097 Jun  6 19:08 encoder\n",
+                        "drwxr-xr-x 2 root root      4096 Jun  6 19:07 metadata\n",
+                        "-rw-r--r-- 1 root root    791656 Jun  6 19:09 t5_spp\n"
+                    ]
+                }
+            ],
+            "source": [
+                "! ls -l {MODEL_NAME}_spark_nlp"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "3R_rS8Fj4W7N"
+            },
+            "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 T5 model 😊"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
+                "id": "uxSo5-b24W7N",
+                "outputId": "b353a436-ccdd-4196-bc84-c774b19c886a"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "+-----------------------------------------------------------------------------------------------------------+\n",
+                        "|result                                                                                                     |\n",
+                        "+-----------------------------------------------------------------------------------------------------------+\n",
+                        "|[We introduce a unified framework that converts text-to-text language problems into a text-to-text format.]|\n",
+                        "+-----------------------------------------------------------------------------------------------------------+\n",
+                        "\n"
+                    ]
+                }
+            ],
+            "source": [
+                "import sparknlp\n",
+                "from sparknlp.base import *\n",
+                "from sparknlp.annotator import *\n",
+                "from pyspark.ml import Pipeline\n",
+                "\n",
+                "test_data = spark.createDataFrame([\n",
+                "    [\"Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a \" +\n",
+                "       \"downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness\" +\n",
+                "       \" of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this \" +\n",
+                "       \"paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework \" +\n",
+                "       \"that converts all text-based language problems into a text-to-text format. Our systematic study compares \" +\n",
+                "       \"pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens \" +\n",
+                "       \"of language understanding tasks. By combining the insights from our exploration with scale and our new \" +\n",
+                "       \"Colossal Clean Crawled Corpus, we achieve state-of-the-art results on many benchmarks covering \" +\n",
+                "       \"summarization, question answering, text classification, and more. To facilitate future work on transfer \" +\n",
+                "       \"learning for NLP, we release our data set, pre-trained models, and code.\"]\n",
+                "]).toDF(\"text\")\n",
+                "\n",
+                "\n",
+                "document_assembler = DocumentAssembler() \\\n",
+                "    .setInputCol(\"text\")\\\n",
+                "    .setOutputCol(\"document\")\n",
+                "\n",
+                "T5 = T5Transformer.load(f\"{MODEL_NAME}_spark_nlp\") \\\n",
+                "  .setInputCols([\"document\"]) \\\n",
+                "  .setOutputCol(\"summary\")\n",
+                "\n",
+                "pipeline = Pipeline().setStages([document_assembler, T5])\n",
+                "\n",
+                "result = pipeline.fit(test_data).transform(test_data)\n",
+                "result.select(\"summary.result\").show(truncate=False)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "PdvQAAfo4W7N"
+            },
+            "source": [
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diff --git a/examples/python/transformers/openvino/HuggingFace_OpenVINO_in_Spark_NLP_XLM_RoBERTa.ipynb b/examples/python/transformers/openvino/HuggingFace_OpenVINO_in_Spark_NLP_XLM_RoBERTa.ipynb
new file mode 100644
index 00000000000000..9757cb28a7af05
--- /dev/null
+++ b/examples/python/transformers/openvino/HuggingFace_OpenVINO_in_Spark_NLP_XLM_RoBERTa.ipynb
@@ -0,0 +1,2353 @@
+{
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "tGk3flXBkgA1"
+            },
+            "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/openvino/HuggingFace_OpenVINO_in_Spark_NLP_XLM-RoBERTa.ipynb)\n",
+                "\n",
+                "# Import OpenVINO XLM-RoBERTa models from HuggingFace 🤗 into Spark NLP 🚀\n",
+                "\n",
+                "This notebook provides a detailed walkthrough on optimizing and exporting XlmRoBerta models from HuggingFace for use in Spark NLP, leveraging the various tools provided in the [Intel OpenVINO toolkit](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html) ecosystem.\n",
+                "\n",
+                "Let's keep in mind a few things before we start 😊\n",
+                "\n",
+                "- OpenVINO support was introduced in  `Spark NLP 5.4.0`, enabling high performance inference for models. Please make sure you have upgraded to the latest Spark NLP release.\n",
+                "- You can import models for XLM-RoBERTa from HuggingFace and they have to be in `Fill Mask` category. Meaning, you cannot use XLM-RoBERTa models trained/fine-tuned on a specific task such as token/sequence classification."
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "xwUb8_YgkgA3"
+            },
+            "source": [
+                "## 1. Export and Save HuggingFace model"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "1btkdcgDkgA4"
+            },
+            "source": [
+                "- Let's install `transformers` and `openvino` packages with other dependencies. You don't need `openvino` to be installed for Spark NLP, however, we need it to load and save models from HuggingFace.\n",
+                "- We lock `transformers` on version `4.41.2`. This doesn't mean it won't work with the future releases, but we wanted you to know which versions have been tested successfully."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 1,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
+                "id": "Ob-7qVD7kgA4",
+                "outputId": "26ec53b9-29f8-4b8b-a457-c5239265c878"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m215.7/215.7 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+                        "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m38.7/38.7 MB\u001b[0m \u001b[31m11.0 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[31m51.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+                        "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m418.4/418.4 kB\u001b[0m \u001b[31m30.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+                        "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.1/542.1 kB\u001b[0m \u001b[31m34.1 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[31m39.4 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[31m12.2 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[31m3.4 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[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+                        "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m11.8 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[31m4.3 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[31m41.2 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[31m10.8 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",
+                        "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\u001b[0m\u001b[31m\n",
+                        "\u001b[0m"
+                    ]
+                }
+            ],
+            "source": [
+                "!pip install -q --upgrade transformers==4.41.2\n",
+                "!pip install -q --upgrade openvino==2024.1\n",
+                "!pip install -q --upgrade optimum-intel\n",
+                "!pip install -q --upgrade sentencepiece"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "B4_lpbejkgA6"
+            },
+            "source": [
+                "[Optimum Intel](https://github.com/huggingface/optimum-intel?tab=readme-ov-file#openvino) is the interface between the Transformers library and the various model optimization and acceleration tools provided by Intel. HuggingFace models loaded with optimum-intel are automatically optimized for OpenVINO, while being compatible with the Transformers API.\n",
+                "- To load a HuggingFace model directly for inference/export, just replace the `AutoModelForXxx` class with the corresponding `OVModelForXxx` class. We can use this to import and export OpenVINO models with `from_pretrained` and `save_pretrained`.\n",
+                "- By setting `export=True`, the source model is converted to OpenVINO IR format on the fly.\n",
+                "- We'll use [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) model from HuggingFace as an example and load it directly as an `OVModelForFeatureExtraction`, representing an OpenVINO model. By setting `export=True`, the available source model is converted to the OpenVINO IR format on the fly.\n",
+                "- In addition to the XLM-RoBERTa model, we also need to save the `XLMRobertaTokenizer`. This is the same for every model, these are assets (saved in `/assets`) needed for tokenization inside Spark NLP."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 2,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/",
+                    "height": 384,
+                    "referenced_widgets": [
+                        "f0b29c4e9ce040b0887b684d8107806c",
+                        "568a20e72853406e98cf3b56fa8dcfed",
+                        "bf2ea669c0f34fcd9c0a6ec9bd734ee5",
+                        "460a00aca09a40ffa111770626e29d36",
+                        "af9515fae3e84f61938756309adb31e9",
+                        "e29b8d2a26a74134b26debcfeb189f67",
+                        "5becc09bedd84464a25f8ee8e87302cf",
+                        "08e3a6f0bc9749ef8494d0a8d54f1988",
+                        "03ce8725c2cc471b9d679f054ff440e3",
+                        "2e33cb328e2c417c9e21de2ea1582dbe",
+                        "5544fd1e03124f3b927d06f2c2168309",
+                        "798158942a2d4960a831ca606cb984c9",
+                        "9e91292a506248c7b0235efb53e4c287",
+                        "bd631ca33e054362a72f7c2fa7d59eb2",
+                        "ee30ed6cb1f04984a9f0937f034d5c44",
+                        "881cfbf6e5bc4608922f348aed97ac9a",
+                        "e3db924a566a47d493022facc2fe3604",
+                        "5d5524a57c734fd88cf4db0875469a95",
+                        "38a880fde73c4f4f9d676f677cfd2b33",
+                        "e7695fd717dc42a6b6cb2518aaa5f500",
+                        "a36ac357bc334b4781f95efb38443b44",
+                        "f63f907574f541758c5bf7e4c178839d",
+                        "fdf82b27b0de483fa5c47bd47d9e1a7a",
+                        "72cea27ab5364b308e48446f7e2a50dd",
+                        "de1c256c5f824ab6b5a1d370d0d52a99",
+                        "c8cb4165fa1e4be485e602472e15f411",
+                        "dd7d488b3dff47f2ac3e84b24ba8c1cd",
+                        "1d15cd383ae246db8b27d01405b2f023",
+                        "2a8d2df1a8834b2ca4be91cc720843f3",
+                        "84e4101cd5644d789ea39e21451210c8",
+                        "bc6ffe22c66948de8c7dae1a78faf9be",
+                        "d837f55706314fd5887d92c08757b287",
+                        "1a76c01145944124af68cfb5d1e9a46b",
+                        "64b434feca3f4cd795d85fd65f1b8c78",
+                        "1b5f7e0521cb4c95835c2322564aa0ec",
+                        "ec00add2ab3646b0be1b9a330f5c44b9",
+                        "cc65d8949f204a3eac0b44729a34d776",
+                        "0c6981ff37be4a20a39e35894c6c90ab",
+                        "27397c7a87e04a5190d9910eecabbedc",
+                        "eba458be3f5c46389e1bca402bd51d1f",
+                        "83fb46a9ff3544b9b9bd4288425d049a",
+                        "aec24ecc2f034f759d455c34f39d97f4",
+                        "a12099e30fbb4c4da6609741934fee70",
+                        "26ee4a4616c944ac92fcbf362273c8eb",
+                        "587af344b6f34368bcbc72dc33a5ff89",
+                        "c9cf37a8f49d48b783b73d94c5fbf916",
+                        "e3de47825af14381ad9bd94cc3b99d20",
+                        "00b41c886a464c169c675abe6799356d",
+                        "8228fafd2e2449e19ce8d576aae8ad5b",
+                        "479acff7b964491d86425b54172ac1ce",
+                        "4720a39dec604038b82403c87ebe9210",
+                        "ba42da872e104416be8481dedf1f55e4",
+                        "2ac2b64e8c5747449802e8791919a316",
+                        "5f737b87d4f64005aae16de7b712c7e9",
+                        "7a92e2b3a294484fb0811d807ba72ecb"
+                    ]
+                },
+                "id": "Hq1EcGX4kgA6",
+                "outputId": "c7abcfb9-5b11-453e-b374-78ade01b316d"
+            },
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
+                        "  warnings.warn(\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "application/vnd.jupyter.widget-view+json": {
+                            "model_id": "f0b29c4e9ce040b0887b684d8107806c",
+                            "version_major": 2,
+                            "version_minor": 0
+                        },
+                        "text/plain": [
+                            "config.json:   0%|          | 0.00/615 [00:00 False\n",
+                        "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead\n",
+                        "  warnings.warn(\n",
+                        "/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py:2246: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
+                        "  assert padding_idx < weight.size(0), \"Padding_idx must be within num_embeddings\"\n",
+                        "Compiling the model to CPU ...\n"
+                    ]
+                }
+            ],
+            "source": [
+                "from optimum.intel import OVModelForFeatureExtraction\n",
+                "from transformers import XLMRobertaTokenizer\n",
+                "\n",
+                "MODEL_NAME = \"xlm-roberta-base\"\n",
+                "EXPORT_PATH = f\"ov_models/{MODEL_NAME}\"\n",
+                "\n",
+                "ov_model = OVModelForFeatureExtraction.from_pretrained(MODEL_NAME, export=True)\n",
+                "tokenizer = XLMRobertaTokenizer.from_pretrained(MODEL_NAME)\n",
+                "\n",
+                "# Save the OpenVINO model\n",
+                "ov_model.save_pretrained(EXPORT_PATH)\n",
+                "tokenizer.save_pretrained(EXPORT_PATH)\n",
+                "\n",
+                "# Create directory for assets and move the tokenizer files.\n",
+                "# A separate folder is needed for Spark NLP.\n",
+                "!mkdir {EXPORT_PATH}/assets\n",
+                "!mv {EXPORT_PATH}/sentencepiece.bpe.model {EXPORT_PATH}/assets/"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "jVx8Z19ikgA7"
+            },
+            "source": [
+                "Let's have a look inside these two directories and see what we are dealing with:"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 3,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
+                "id": "23WVUAczkgA7",
+                "outputId": "194a99d9-b414-416a-fcf9-d99e1741d11f"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "total 1084220\n",
+                        "drwxr-xr-x 2 root root       4096 Jun  5 17:40 assets\n",
+                        "-rw-r--r-- 1 root root        679 Jun  5 17:40 config.json\n",
+                        "-rw-r--r-- 1 root root 1109816508 Jun  5 17:40 openvino_model.bin\n",
+                        "-rw-r--r-- 1 root root     400993 Jun  5 17:40 openvino_model.xml\n",
+                        "-rw-r--r-- 1 root root        280 Jun  5 17:40 special_tokens_map.json\n",
+                        "-rw-r--r-- 1 root root       1172 Jun  5 17:40 tokenizer_config.json\n"
+                    ]
+                }
+            ],
+            "source": [
+                "!ls -l {EXPORT_PATH}"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 4,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
+                "id": "4G0_E6kqkgA8",
+                "outputId": "3050fe0d-659d-4d31-85b7-06c5e38c6180"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "total 4952\n",
+                        "-rw-r--r-- 1 root root 5069051 Jun  5 17:40 sentencepiece.bpe.model\n"
+                    ]
+                }
+            ],
+            "source": [
+                "!ls -l {EXPORT_PATH}/assets"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "zRN-_0wpkgA8"
+            },
+            "source": [
+                "## 2. Import and Save XLM-RoBERTa in Spark NLP\n",
+                "\n",
+                "- 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": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
+                "id": "D0yO3TmwkgA8",
+                "outputId": "fa130f65-719b-484d-f58c-e2d0c8a09b68"
+            },
+            "outputs": [],
+            "source": [
+                "! wget -q http://setup.johnsnowlabs.com/colab.sh -O - | bash"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "7slGi8nrkgA9"
+            },
+            "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": "Z_Pnd_W8kgA9",
+                "outputId": "a14ce1af-cfca-40c5-f0f1-f6315e36d9c7"
+            },
+            "outputs": [],
+            "source": [
+                "import sparknlp\n",
+                "# let's start Spark with Spark NLP\n",
+                "spark = sparknlp.start()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "qfwqBV67kgA9"
+            },
+            "source": [
+                "- Let's use `loadSavedModel` functon in `XlmRoBertaEmbeddings` which allows us to load the OpenVINO model\n",
+                "- Most params will be set automatically. They can also be set later after loading the model in `XlmRoBertaEmbeddings` during runtime, so don't worry about setting them now\n",
+                "- `loadSavedModel` accepts two params, first is the path to the exported model. The second is the SparkSession that is `spark` variable we previously started via `sparknlp.start()`\n",
+                "- `setStorageRef` is very important. When you are training a task like NER or any Text Classification, we use this reference to bound the trained model to this specific embeddings so you won't load a different embeddings by mistake and see terrible results 😊\n",
+                "- It's up to you what you put in `setStorageRef` but it cannot be changed later on. We usually use the name of the model to be clear, but you can get creative if you want!\n",
+                "- The `dimension` param is is purely cosmetic and won't change anything. It's mostly for you to know later via `.getDimension` what is the dimension of your model. So set this accordingly.\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.st 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"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 7,
+            "metadata": {
+                "id": "8CqvC6sJkgA9"
+            },
+            "outputs": [],
+            "source": [
+                "from sparknlp.annotator import *\n",
+                "\n",
+                "# All these params should be identical to the original model\n",
+                "xlm_roberta = XlmRoBertaEmbeddings.loadSavedModel(f\"{EXPORT_PATH}\", spark)\\\n",
+                "    .setInputCols([\"document\",'token'])\\\n",
+                "    .setOutputCol(\"xlm_roberta\")\\\n",
+                "    .setCaseSensitive(True)\\\n",
+                "    .setDimension(768)\\\n",
+                "    .setStorageRef('xlm_roberta_base')"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "8GWj_urkkgA9"
+            },
+            "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": 8,
+            "metadata": {
+                "id": "ZmiaXQXKkgA-"
+            },
+            "outputs": [],
+            "source": [
+                "xlm_roberta.write().overwrite().save(f\"{MODEL_NAME}_spark_nlp\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "Z0aGPBmVkgA-"
+            },
+            "source": [
+                "Let's clean up stuff we don't need anymore"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 9,
+            "metadata": {
+                "id": "k2yCZptPkgA-"
+            },
+            "outputs": [],
+            "source": [
+                "!rm -rf {EXPORT_PATH}"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "XPsHMXy4kgA-"
+            },
+            "source": [
+                "Awesome  😎 !\n",
+                "\n",
+                "This is your OpenVINO XLM-RoBERTa model from HuggingFace 🤗  loaded and saved by Spark NLP 🚀"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 10,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
+                "id": "Iajnc2gekgA-",
+                "outputId": "ff613b7b-08f3-4fbe-dbe8-ce86cf575fbd"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "total 1089324\n",
+                        "drwxr-xr-x 2 root root       4096 Jun  5 17:52 metadata\n",
+                        "-rw-r--r-- 1 root root 1110387189 Jun  5 17:53 xlmroberta_openvino\n",
+                        "-rw-r--r-- 1 root root    5069051 Jun  5 17:53 xlmroberta_spp\n"
+                    ]
+                }
+            ],
+            "source": [
+                "! ls -l {MODEL_NAME}_spark_nlp"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "o71ap_SXkgA-"
+            },
+            "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 XLM-RoBERTa model 😊"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 11,
+            "metadata": {
+                "id": "eIlcw7nnkgA-"
+            },
+            "outputs": [],
+            "source": [
+                "import sparknlp\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",
+                "xlm_roberta_loaded = XlmRoBertaEmbeddings.load(f\"{MODEL_NAME}_spark_nlp\")\\\n",
+                "    .setInputCols([\"document\",'token'])\\\n",
+                "    .setOutputCol(\"xlm_roberta\")\\\n",
+                "\n",
+                "pipeline = Pipeline(\n",
+                "    stages = [\n",
+                "        document_assembler,\n",
+                "        tokenizer,\n",
+                "        xlm_roberta_loaded\n",
+                "  ])\n",
+                "\n",
+                "data = spark.createDataFrame([['William Henry Gates III (born October 28, 1955) is an American business magnate, software developer, investor,and philanthropist.']]).toDF(\"text\")\n",
+                "model = pipeline.fit(data)\n",
+                "result = model.transform(data)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 12,
+            "metadata": {
+                "colab": {
+                    "base_uri": "https://localhost:8080/"
+                },
+                "id": "37gi-KXfkgA_",
+                "outputId": "e235acd7-d417-4a76-c0b6-66c29841f2ea"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "+--------------------+\n",
+                        "|          embeddings|\n",
+                        "+--------------------+\n",
+                        "|[0.017810468, 0.1...|\n",
+                        "|[-0.005121568, 0....|\n",
+                        "|[0.0051700654, 0....|\n",
+                        "|[0.006572605, 0.1...|\n",
+                        "|[-0.028698405, 0....|\n",
+                        "|[-0.0055642026, 0...|\n",
+                        "|[-0.017623411, 0....|\n",
+                        "|[-0.11884114, 0.0...|\n",
+                        "|[-0.08074665, 0.1...|\n",
+                        "|[-0.034696482, 0....|\n",
+                        "|[-0.06809629, 0.1...|\n",
+                        "|[-0.050851095, 0....|\n",
+                        "|[-0.006526501, 0....|\n",
+                        "|[-0.02970995, 0.1...|\n",
+                        "|[0.011362048, 0.2...|\n",
+                        "|[0.044628035, 0.5...|\n",
+                        "|[0.022998871, 0.2...|\n",
+                        "|[0.017431622, 0.2...|\n",
+                        "|[-0.02495086, 0.1...|\n",
+                        "|[-0.03151579, 0.1...|\n",
+                        "+--------------------+\n",
+                        "only showing top 20 rows\n",
+                        "\n"
+                    ]
+                }
+            ],
+            "source": [
+                "result.selectExpr(\"explode(xlm_roberta.embeddings) as embeddings\").show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "id": "SMP8yiKhkgA_"
+            },
+            "source": [
+                "That's it! You can now go wild and use hundreds of XLM-RoBERTa models from HuggingFace 🤗 in Spark NLP 🚀\n"
+            ]
+        }
+    ],
+    "metadata": {
+        "colab": {
+            "provenance": []
+        },
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