notebooks/244-named-entity-recognition/244-named-entity-recognition.ipynb

{
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   "source": [
    "# Named entity recognition with OpenVINO™\n",
    "\n",
    "\n",
    "The Named Entity Recognition(NER) is a natural language processing method that involves the detecting of key information in the unstructured text and categorizing it into pre-defined categories. These categories or named entities refer to the key subjects of text, such as names, locations, companies and etc.\n",
    "\n",
    "NER is a good method for the situations when a high-level overview of a large amount of text is needed. NER can be helpful with such task as analyzing key information in unstructured text or automates the information extraction of large amounts of data.\n",
    "\n",
    "\n",
    "This tutorial shows how to perform named entity recognition using OpenVINO. We will use the pre-trained model [`elastic/distilbert-base-cased-finetuned-conll03-english`](https://huggingface.co/elastic/distilbert-base-cased-finetuned-conll03-english). It is DistilBERT based model, trained on [`conll03 english dataset`](https://huggingface.co/datasets/conll2003). The model can recognize four named entities in text: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups. The model is sensitive to capital letters.\n",
    "\n",
    "To simplify the user experience, the [Hugging Face Optimum](https://huggingface.co/docs/optimum) library is used to convert the model to OpenVINO™ IR format and quantize it.\n",
    "\n",
    "#### Table of content:- [Prerequisites](#Prerequisites-Uparrow)\n",
    "- [Download the NER model](#Download-the-NER-model-Uparrow)\n",
    "- [Quantize the model, using Hugging Face Optimum API](#Quantize-the-model,-using-Hugging-Face-Optimum-API-Uparrow)\n",
    "- [Prepare demo for Named Entity Recognition OpenVINO Runtime](#Prepare-demo-for-Named-Entity-Recognition-OpenVINO-Runtime-Uparrow)\n",
    "- [Compare the Original and Quantized Models](#Compare-the-Original-and-Quantized-Models-Uparrow)\n",
    "    - [Compare performance](#Compare-performance-Uparrow)\n",
    "    - [Compare size of the models](#Compare-size-of-the-models-Uparrow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites [$\\Uparrow$](#Table-of-content:)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -q \"diffusers>=0.17.1\" \"openvino>=2023.1.0\" \"nncf>=2.5.0\" \"gradio\" \"onnx>=1.11.0\" \"onnxruntime>=1.14.0\" \"transformers>=4.31.0\"\n",
    "%pip install -q \"git+https://github.com/huggingface/optimum-intel.git\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download the NER model [$\\Uparrow$](#Table-of-content:)\n",
    "\n",
    "We load the [`distilbert-base-cased-finetuned-conll03-english`](https://huggingface.co/elastic/distilbert-base-cased-finetuned-conll03-english) model from the [Hugging Face Hub](https://huggingface.co/models) with [Hugging Face Transformers library](https://huggingface.co/docs/transformers/index).\n",
    "\n",
    "Model class initialization starts with calling `from_pretrained` method. To easily save the model, you can use the `save_pretrained()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-09-19 19:03:57.913343: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2023-09-19 19:03:57.950536: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-09-19 19:03:58.511125: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForTokenClassification\n",
    "\n",
    "model_id = \"elastic/distilbert-base-cased-finetuned-conll03-english\"\n",
    "model = AutoModelForTokenClassification.from_pretrained(model_id)\n",
    "\n",
    "original_ner_model_dir = 'original_ner_model'\n",
    "model.save_pretrained(original_ner_model_dir)\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantize the model, using Hugging Face Optimum API [$\\Uparrow$](#Table-of-content:)\n",
    "\n",
    "Post-training static quantization introduces an additional calibration step where data is fed through the network in order to compute the activations quantization parameters. For quantization it will be used [Hugging Face Optimum Intel API](https://huggingface.co/docs/optimum/intel/index).\n",
    "\n",
    "To handle the NNCF quantization process we use class [`OVQuantizer`](https://huggingface.co/docs/optimum/intel/reference_ov#optimum.intel.OVQuantizer). The quantization with Hugging Face Optimum Intel API contains the next steps:\n",
    "* Model class initialization starts with calling `from_pretrained()` method.\n",
    "* Next we create calibration dataset with `get_calibration_dataset()` to use for the post-training static quantization calibration step. \n",
    "* After we quantize a model and save the resulting model in the OpenVINO IR format to save_directory with `quantize()` method. \n",
    "* Then we load the quantized model. The Optimum Inference models are API compatible with Hugging Face Transformers models and we can just replace `AutoModelForXxx` class with the corresponding `OVModelForXxx` class. So we use `OVModelForTokenClassification` to load the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'\n",
      "/home/ea/work/ov_venv/lib/python3.8/site-packages/transformers/deepspeed.py:23: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations\n",
      "  warnings.warn(\n",
      "Found cached dataset conll2003 (/home/ea/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/9a4d16a94f8674ba3466315300359b0acd891b68b6c8743ddf60b9c702adce98)\n",
      "Loading cached shuffled indices for dataset at /home/ea/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/9a4d16a94f8674ba3466315300359b0acd891b68b6c8743ddf60b9c702adce98/cache-2fe5320fac60946d.arrow\n"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No configuration describing the quantization process was provided, a default OVConfig will be generated.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "INFO:nncf:Compiling and loading torch extension: quantized_functions_cpu...\n",
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "INFO:nncf:Finished loading torch extension: quantized_functions_cpu\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using framework PyTorch: 2.0.1+cpu\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.\n",
      "/home/ea/work/ov_venv/lib/python3.8/site-packages/nncf/torch/dynamic_graph/wrappers.py:81: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
      "  result = operator(*args, **kwargs)\n",
      "Configuration saved in quantized_ner_model/openvino_config.json\n",
      "Compiling the model...\n",
      "Set CACHE_DIR to quantized_ner_model/model_cache\n"
     ]
    }
   ],
   "source": [
    "from functools import partial\n",
    "from optimum.intel import OVQuantizer\n",
    "\n",
    "from optimum.intel import OVModelForTokenClassification\n",
    "\n",
    "def preprocess_fn(data, tokenizer):\n",
    "    examples = []\n",
    "    for data_chunk in data[\"tokens\"]:\n",
    "        examples.append(' '.join(data_chunk))\n",
    "\n",
    "    return tokenizer(\n",
    "        examples, padding=True, truncation=True, max_length=128\n",
    "    )\n",
    "\n",
    "quantizer = OVQuantizer.from_pretrained(model)\n",
    "calibration_dataset = quantizer.get_calibration_dataset(\n",
    "    \"conll2003\",\n",
    "    preprocess_function=partial(preprocess_fn, tokenizer=tokenizer),\n",
    "    num_samples=100,\n",
    "    dataset_split=\"train\",\n",
    "    preprocess_batch=True,\n",
    ")\n",
    "\n",
    "# The directory where the quantized model will be saved\n",
    "quantized_ner_model_dir = \"quantized_ner_model\"\n",
    "\n",
    "# Apply static quantization and save the resulting model in the OpenVINO IR format\n",
    "quantizer.quantize(calibration_dataset=calibration_dataset, save_directory=quantized_ner_model_dir)\n",
    "\n",
    "# Load the quantized model\n",
    "optimized_model = OVModelForTokenClassification.from_pretrained(quantized_ner_model_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare demo for Named Entity Recognition OpenVINO Runtime [$\\Uparrow$](#Table-of-content:)\n",
    "\n",
    "As the Optimum Inference models are API compatible with Hugging Face Transformers models, we can just use `pipleine()` from [Hugging Face Transformers API](https://huggingface.co/docs/transformers/index) for inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "ner_pipeline_optimized = pipeline(\"token-classification\", model=optimized_model, tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, you can try NER model on own text. Put your sentence to input text box, click Submit button, the model label the recognized entities in the text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [],
    "test_replace": {
     "demo.launch(debug=True)": "demo.launch()",
     "demo.launch(share=True, debug=True)": "demo.launch(share=True)"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7860\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keyboard interruption in main thread... closing server.\n"
     ]
    }
   ],
   "source": [
    "import gradio as gr\n",
    "\n",
    "examples = [\n",
    "    \"My name is Wolfgang and I live in Berlin.\",\n",
    "]\n",
    "\n",
    "def run_ner(text):\n",
    "    output = ner_pipeline_optimized(text)\n",
    "    return {\"text\": text, \"entities\": output} \n",
    "\n",
    "demo = gr.Interface(run_ner,\n",
    "                    gr.Textbox(placeholder=\"Enter sentence here...\", label=\"Input Text\"), \n",
    "                    gr.HighlightedText(label=\"Output Text\"),\n",
    "                    examples=examples,\n",
    "                    allow_flagging=\"never\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    try:\n",
    "        demo.launch(debug=True)\n",
    "    except Exception:\n",
    "        demo.launch(share=True, debug=True)\n",
    "# if you are launching remotely, specify server_name and server_port\n",
    "# demo.launch(server_name='your server name', server_port='server port in int')\n",
    "# Read more in the docs: https://gradio.app/docs/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare the Original and Quantized Models [$\\Uparrow$](#Table-of-content:)\n",
    "\n",
    "Compare the original [`distilbert-base-cased-finetuned-conll03-english`](https://huggingface.co/elastic/distilbert-base-cased-finetuned-conll03-english) model with quantized and converted to OpenVINO IR format models to see the difference."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare performance [$\\Uparrow$](#Table-of-content:)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ner_pipeline_original = pipeline(\"token-classification\", model=model, tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median inference time of quantized model: 0.008145123501890339 \n",
      "Median inference time of original model: 0.09339697850373341 \n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "\n",
    "def calc_perf(ner_pipeline):\n",
    "    inference_times = []\n",
    "\n",
    "    for data in calibration_dataset:\n",
    "        text = ' '.join(data['tokens'])\n",
    "        start = time.perf_counter()\n",
    "        ner_pipeline(text)\n",
    "        end = time.perf_counter()\n",
    "        inference_times.append(end - start)\n",
    "\n",
    "    return np.median(inference_times)\n",
    "\n",
    "\n",
    "print(\n",
    "    f\"Median inference time of quantized model: {calc_perf(ner_pipeline_optimized)} \"\n",
    ")\n",
    "\n",
    "print(\n",
    "    f\"Median inference time of original model: {calc_perf(ner_pipeline_original)} \"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare size of the models [$\\Uparrow$](#Table-of-content:)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of original model in Bytes is 260824741\n",
      "Size of quantized model in Bytes is 133539000\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "print(f'Size of original model in Bytes is {Path(original_ner_model_dir, \"pytorch_model.bin\").stat().st_size}')\n",
    "print(f'Size of quantized model in Bytes is {Path(quantized_ner_model_dir, \"openvino_model.bin\").stat().st_size}')"
   ]
  }
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