training/lilt_funsd.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Document AI: Fine-tuning LiLT for document-understanding using Hugging Face Transformers \n",
    "_Document AI: LiLT a better language agnostic LayoutLM model_ \n",
    "\n",
    "In this blog, you will learn how to fine-tune [LiLt](https://huggingface.co/docs/transformers/v4.24.0/en/model_doc/lilt#lilt) for document-understand using Hugging Face Transformers. LiLt or **L**anguage **i**ndependent **L**ayout **T**ransformer can combine any pre-trained [RoBERTa](https://huggingface.co/models?other=roberta) text encoder with a lightweight Layout Transformer, to enable document understanding and information extraction for any language. \n",
    "This means you can use non-English RoBERTa checkpoints, e.g. [bertin-project/bertin-roberta-base-spanish](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) for document understanding tasks. To convert a RoBERTa checkpoint to a LiLT checkpoint, you can follow [this guide](https://github.com/jpWang/LiLT#or-generate-your-own-checkpoint-optional).\n",
    "[LiLt](https://huggingface.co/docs/transformers/v4.24.0/en/model_doc/lilt#lilt) is released with an MIT license and is available on the [Hugging Face Hub](https://huggingface.co/models?other=lilt). \n",
    "\n",
    "In this example we will use the [FUNSD](https://guillaumejaume.github.io/FUNSD/) dataset a collection of 199 fully annotated forms. More information for the dataset can be found at the [dataset page](https://guillaumejaume.github.io/FUNSD/).\n",
    "\n",
    "You will learn how to:\n",
    "\n",
    "1. [Setup Development Environment](#1-setup-development-environment)\n",
    "2. [Load and prepare FUNSD dataset](#2-load-sroie-dataset)\n",
    "3. [Fine-tune and evaluate LiLT](#4-fine-tune-and-evaluate-lilt-model)\n",
    "\n",
    "Before we can start, make sure you have a [Hugging Face Account](https://huggingface.co/join) to save artifacts and experiments. \n",
    "\n",
    "## Quick intro: LiLT Language-independent Layout Transformer \n",
    "\n",
    "LiLT is a language independent Transformer model for document image understanding and information extraction transformers and can be used form understanding and receipt understanding. LiLT can be pretrained on the structured documents of a single language and then directly fine-tuned on other languages with the corresponding offthe-shelf monolingual/multilingual pre-trained textual models. \n",
    "\n",
    "![lilt](../assets/lilt.png)\n",
    "\n",
    "* Paper: https://arxiv.org/abs/2202.13669\n",
    "* Official repo: https://github.com/jpwang/lilt\n",
    "\n",
    "--- \n",
    "\n",
    "Now we know how LiLT works, let's get started. 🚀\n",
    "\n",
    "_Note: This tutorial was created and run on a g4dn.xlarge AWS EC2 Instance including a NVIDIA T4._\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Setup Development Environment\n",
    "\n",
    "Our first step is to install the Hugging Face Libraries, including transformers and datasets. Running the following cell will install all the required packages. \n",
    "Additinoally, we need to install an OCR-library to extract text from images. We will use [pytesseract](https://pypi.org/project/pytesseract/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ubuntu\n",
    "!sudo apt install -y tesseract-ocr\n",
    "# python\n",
    "!pip install pytesseract transformers datasets seqeval tensorboard --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# install git-fls for pushing model and logs to the hugging face hub\n",
    "!sudo apt-get install git-lfs --yes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example will use the [Hugging Face Hub](https://huggingface.co/models) as a remote model versioning service. To be able to push our model to the Hub, you need to register on the [Hugging Face](https://huggingface.co/join). \n",
    "If you already have an account, you can skip this step. \n",
    "After you have an account, we will use the `notebook_login` util from the `huggingface_hub` package to log into our account and store our token (access key) on the disk. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load and prepare FUNSD dataset\n",
    "\n",
    "We will use the [FUNSD](https://guillaumejaume.github.io/FUNSD/) dataset a collection of 199 fully annotated forms. The dataset is available on Hugging Face at [nielsr/funsd](https://huggingface.co/datasets/nielsr/funsd) and [nielsr/funsd-layoutlmv3](https://huggingface.co/datasets/nielsr/funsd-layoutlmv3). We will use the `nielsr/funsd-layoutlmv3`, which includes segment positions, which will help in boosting the performance (as shown in [this paper](https://arxiv.org/abs/2105.11210)).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataset_id =\"nielsr/funsd\"\n",
    "dataset_id =\"nielsr/funsd-layoutlmv3\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To load the `funsd` dataset, we use the `load_dataset()` method from the 🤗 Datasets library.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a59429d4babe48ccbafba0de22c2a9e3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/5.13k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset funsd-layoutlmv3/funsd to /home/ubuntu/.cache/huggingface/datasets/nielsr___funsd-layoutlmv3/funsd/1.0.0/0e3f4efdfd59aa1c3b4952c517894f7b1fc4d75c12ef01bcc8626a69e41c1bb9...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d82c6982f6eb4a9da086a81f8c07ad7f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d593d158e37a464cb7d34fa1ec2912a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset funsd-layoutlmv3 downloaded and prepared to /home/ubuntu/.cache/huggingface/datasets/nielsr___funsd-layoutlmv3/funsd/1.0.0/0e3f4efdfd59aa1c3b4952c517894f7b1fc4d75c12ef01bcc8626a69e41c1bb9. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "37292c23265f428cbb08b82653edec1a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset size: 149\n",
      "Test dataset size: 50\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "\n",
    "dataset = load_dataset(dataset_id)\n",
    "\n",
    "print(f\"Train dataset size: {len(dataset['train'])}\")\n",
    "print(f\"Test dataset size: {len(dataset['test'])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets checkout an example of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=350x450>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from PIL import Image, ImageDraw, ImageFont\n",
    "\n",
    "image = dataset['train'][34]['image']\n",
    "image = image.convert(\"RGB\")\n",
    "image.resize((350,450))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can display all our classes by inspecting the features of our dataset. Those `ner_tags` will be later used to create a user friendly output after we fine-tuned our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available labels: ['O', 'B-HEADER', 'I-HEADER', 'B-QUESTION', 'I-QUESTION', 'B-ANSWER', 'I-ANSWER']\n"
     ]
    }
   ],
   "source": [
    "labels = dataset['train'].features['ner_tags'].feature.names\n",
    "print(f\"Available labels: {labels}\")\n",
    "\n",
    "id2label = {v: k for v, k in enumerate(labels)}\n",
    "label2id = {k: v for v, k in enumerate(labels)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To train our model we need to convert our inputs (text/image) to token IDs. This is done by a 🤗 Transformers Tokenizer and PyTesseract. If you are not sure what this means check out [chapter 6](https://huggingface.co/course/chapter6/1?fw=tf) of the Hugging Face Course.\n",
    "\n",
    "_Note: The LiLT model doesn't have a `AutoProcessor` or `Tokenizer` to nicely create our input documents, but we can use the `LayoutLMv3Processor` or `LayoutLMv2Processor` instead._  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import LayoutLMv3FeatureExtractor, AutoTokenizer, LayoutLMv3Processor\n",
    "\n",
    "model_id=\"SCUT-DLVCLab/lilt-roberta-en-base\"\n",
    "\n",
    "# use LayoutLMv3 processor without ocr since the dataset already includes the ocr text\n",
    "feature_extractor = LayoutLMv3FeatureExtractor(apply_ocr=False) # set \n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
    "# cannot use from_pretrained since the processor is not saved in the base model\n",
    "processor = LayoutLMv3Processor(feature_extractor, tokenizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we can process our dataset we need to define the `features` or the processed inputs, which are later based into the model. Features are a special dictionary that defines the internal structure of a dataset.\n",
    "Compared to traditional NLP datasets we need to add the `bbox` feature, which is a 2D array of the bounding boxes for each token. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "835f9f0f1e4348f6be706826b8c23c53",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/149 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7579460a4bf74ebdaf966a85451d7323",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['input_ids', 'attention_mask', 'bbox', 'labels'])\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "from functools import partial\n",
    "from datasets import Features, Sequence, ClassLabel, Value, Array2D\n",
    "\n",
    "\n",
    "# we need to define custom features\n",
    "features = Features(\n",
    "    {\n",
    "        \"input_ids\": Sequence(feature=Value(dtype=\"int64\")),\n",
    "        \"attention_mask\": Sequence(feature=Value(dtype=\"int64\")),\n",
    "        \"bbox\": Array2D(dtype=\"int64\", shape=(512, 4)),\n",
    "        \"labels\": Sequence(ClassLabel(names=labels)),\n",
    "    }\n",
    ")\n",
    "\n",
    "# preprocess function to perpare into the correct format for the model\n",
    "def process(sample, processor=None):\n",
    "    encoding = processor(\n",
    "        sample[\"image\"].convert(\"RGB\"),\n",
    "        sample[\"tokens\"],\n",
    "        boxes=sample[\"bboxes\"],\n",
    "        word_labels=sample[\"ner_tags\"],\n",
    "        padding=\"max_length\",\n",
    "        truncation=True,\n",
    "    )\n",
    "    # remove pixel values not needed for LiLT\n",
    "    del encoding[\"pixel_values\"]\n",
    "    return encoding\n",
    "\n",
    "\n",
    "# process the dataset and format it to pytorch\n",
    "proc_dataset = dataset.map(\n",
    "    partial(process, processor=processor),\n",
    "    remove_columns=[\"image\", \"tokens\", \"ner_tags\", \"id\", \"bboxes\"],\n",
    "    features=features,\n",
    ").with_format(\"torch\")\n",
    "\n",
    "print(proc_dataset[\"train\"].features.keys())\n",
    "# dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox','lables'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Fine-tune and evaluate LiLT\n",
    "\n",
    "After we have processed our dataset, we can start training our model. Therefore we first need to load the [SCUT-DLVCLab/lilt-roberta-en-base](SCUT-DLVCLab/lilt-roberta-en-base) model, which is based on a English RoBERTa model with the `LiltForTokenClassification` class with the label mapping of our dataset. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of LiltForTokenClassification were not initialized from the model checkpoint at SCUT-DLVCLab/lilt-roberta-en-base and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "from transformers import LiltForTokenClassification\n",
    "\n",
    "# huggingface hub model id\n",
    "model_id = \"SCUT-DLVCLab/lilt-roberta-en-base\"\n",
    "\n",
    "# load model with correct number of labels and mapping\n",
    "model = LiltForTokenClassification.from_pretrained(\n",
    "    model_id, num_labels=len(labels), label2id=label2id, id2label=id2label\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to evaluate our model during training. The `Trainer` supports evaluation during training by providing a `compute_metrics`. \n",
    "We are going to use `seqeval` and the `evaluate` library to evaluate the overall f1 score for all tokens. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import evaluate\n",
    "import numpy as np\n",
    "\n",
    "# load seqeval metric\n",
    "metric = evaluate.load(\"seqeval\")\n",
    "\n",
    "# labels of the model\n",
    "ner_labels = list(model.config.id2label.values())\n",
    "\n",
    "\n",
    "def compute_metrics(p):\n",
    "    predictions, labels = p\n",
    "    predictions = np.argmax(predictions, axis=2)\n",
    "\n",
    "    all_predictions = []\n",
    "    all_labels = []\n",
    "    for prediction, label in zip(predictions, labels):\n",
    "        for predicted_idx, label_idx in zip(prediction, label):\n",
    "            if label_idx == -100:\n",
    "                continue\n",
    "            all_predictions.append(ner_labels[predicted_idx])\n",
    "            all_labels.append(ner_labels[label_idx])\n",
    "    return metric.compute(predictions=[all_predictions], references=[all_labels])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last step is to define the hyperparameters (`TrainingArguments`) we want to use for our training. We are leveraging the [Hugging Face Hub](https://huggingface.co/models) integration of the `Trainer` to automatically push our checkpoints, logs and metrics during training into a repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from huggingface_hub import HfFolder\n",
    "from transformers import Trainer, TrainingArguments\n",
    "\n",
    "# hugging face parameter\n",
    "repository_id = \"lilt-en-funsd\"\n",
    "\n",
    "# Define training args\n",
    "training_args = TrainingArguments(\n",
    "    output_dir=repository_id,\n",
    "    per_device_train_batch_size=8,\n",
    "    per_device_eval_batch_size=8,\n",
    "    fp16=True,\n",
    "    learning_rate=5e-5,\n",
    "    max_steps=2500,\n",
    "    # logging & evaluation strategies\n",
    "    logging_dir=f\"{repository_id}/logs\",\n",
    "    logging_strategy=\"steps\",\n",
    "    logging_steps=200,\n",
    "    evaluation_strategy=\"steps\",\n",
    "    save_strategy=\"steps\",\n",
    "    save_steps=200,\n",
    "    save_total_limit=2,\n",
    "    load_best_model_at_end=True,\n",
    "    metric_for_best_model=\"overall_f1\",\n",
    "    # push to hub parameters\n",
    "    report_to=\"tensorboard\",\n",
    "    push_to_hub=True,\n",
    "    hub_strategy=\"every_save\",\n",
    "    hub_model_id=repository_id,\n",
    "    hub_token=HfFolder.get_token(),\n",
    ")\n",
    "\n",
    "# Create Trainer instance\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=proc_dataset[\"train\"],\n",
    "    eval_dataset=proc_dataset[\"test\"],\n",
    "    compute_metrics=compute_metrics,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can start our training by using the `train` method of the `Trainer`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start training\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "![lilt_training](../assets/lilt_training.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice, we have trained our model. 🎉 Lets run evaluate the best model again on the test set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 50\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='7' max='7' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [7/7 00:00]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"{'precision': 0.8821428571428571, 'recall': 0.9069767441860465, 'f1': 0.8943874471937237, 'number': 817}\" of type <class 'dict'> for key \"eval/ANSWER\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"{'precision': 0.6126126126126126, 'recall': 0.5714285714285714, 'f1': 0.591304347826087, 'number': 119}\" of type <class 'dict'> for key \"eval/HEADER\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"{'precision': 0.9045045045045045, 'recall': 0.9322191272051996, 'f1': 0.9181527206218564, 'number': 1077}\" of type <class 'dict'> for key \"eval/QUESTION\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'eval_loss': 1.6116704940795898,\n",
       " 'eval_ANSWER': {'precision': 0.8821428571428571,\n",
       "  'recall': 0.9069767441860465,\n",
       "  'f1': 0.8943874471937237,\n",
       "  'number': 817},\n",
       " 'eval_HEADER': {'precision': 0.6126126126126126,\n",
       "  'recall': 0.5714285714285714,\n",
       "  'f1': 0.591304347826087,\n",
       "  'number': 119},\n",
       " 'eval_QUESTION': {'precision': 0.9045045045045045,\n",
       "  'recall': 0.9322191272051996,\n",
       "  'f1': 0.9181527206218564,\n",
       "  'number': 1077},\n",
       " 'eval_overall_precision': 0.8796700630761766,\n",
       " 'eval_overall_recall': 0.9006458022851466,\n",
       " 'eval_overall_f1': 0.8900343642611683,\n",
       " 'eval_overall_accuracy': 0.8203970046356829,\n",
       " 'eval_runtime': 1.2437,\n",
       " 'eval_samples_per_second': 40.201,\n",
       " 'eval_steps_per_second': 5.628,\n",
       " 'epoch': 131.58}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.evaluate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best score we achieved is an overall f1 score of `0.89`. For comparison `LayoutLM` (v1) achieves an overall f1 score of `0.79`, thats `12.66%` improvement.\n",
    "\n",
    "Lets save our results and processor to the Hugging Face Hub and create a model card. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change apply_ocr to True to use the ocr text for inference\n",
    "processor.feature_extractor.apply_ocr = True\n",
    "\n",
    "# Save processor and create model card\n",
    "processor.save_pretrained(repository_id)\n",
    "trainer.create_model_card()\n",
    "trainer.push_to_hub()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Run Inference\n",
    "\n",
    "Now we have a trained model, we can use it to run inference. We will create a function that takes a document image and returns the extracted text and the bounding boxes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file config.json from cache at /home/ubuntu/.cache/huggingface/hub/models--philschmid--lilt-en-funsd/snapshots/4c1f7f81f6fef1f9cc52b44da32a7899fab1bfc9/config.json\n",
      "Model config LiltConfig {\n",
      "  \"_name_or_path\": \"SCUT-DLVCLab/lilt-roberta-en-base\",\n",
      "  \"architectures\": [\n",
      "    \"LiltForTokenClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"bos_token_id\": 0,\n",
      "  \"channel_shrink_ratio\": 4,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"O\",\n",
      "    \"1\": \"B-HEADER\",\n",
      "    \"2\": \"I-HEADER\",\n",
      "    \"3\": \"B-QUESTION\",\n",
      "    \"4\": \"I-QUESTION\",\n",
      "    \"5\": \"B-ANSWER\",\n",
      "    \"6\": \"I-ANSWER\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"label2id\": {\n",
      "    \"B-ANSWER\": 5,\n",
      "    \"B-HEADER\": 1,\n",
      "    \"B-QUESTION\": 3,\n",
      "    \"I-ANSWER\": 6,\n",
      "    \"I-HEADER\": 2,\n",
      "    \"I-QUESTION\": 4,\n",
      "    \"O\": 0\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-05,\n",
      "  \"max_2d_position_embeddings\": 1024,\n",
      "  \"max_position_embeddings\": 514,\n",
      "  \"model_type\": \"lilt\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"pad_token_id\": 1,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"torch_dtype\": \"float32\",\n",
      "  \"transformers_version\": \"4.24.0\",\n",
      "  \"type_vocab_size\": 1,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50265\n",
      "}\n",
      "\n",
      "loading weights file pytorch_model.bin from cache at /home/ubuntu/.cache/huggingface/hub/models--philschmid--lilt-en-funsd/snapshots/4c1f7f81f6fef1f9cc52b44da32a7899fab1bfc9/pytorch_model.bin\n",
      "All model checkpoint weights were used when initializing LiltForTokenClassification.\n",
      "\n",
      "All the weights of LiltForTokenClassification were initialized from the model checkpoint at philschmid/lilt-en-funsd.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use LiltForTokenClassification for predictions without further training.\n",
      "loading configuration file preprocessor_config.json from cache at /home/ubuntu/.cache/huggingface/hub/models--philschmid--lilt-en-funsd/snapshots/4c1f7f81f6fef1f9cc52b44da32a7899fab1bfc9/preprocessor_config.json\n",
      "Feature extractor LayoutLMv3FeatureExtractor {\n",
      "  \"apply_ocr\": true,\n",
      "  \"do_normalize\": true,\n",
      "  \"do_resize\": true,\n",
      "  \"feature_extractor_type\": \"LayoutLMv3FeatureExtractor\",\n",
      "  \"image_mean\": [\n",
      "    0.5,\n",
      "    0.5,\n",
      "    0.5\n",
      "  ],\n",
      "  \"image_std\": [\n",
      "    0.5,\n",
      "    0.5,\n",
      "    0.5\n",
      "  ],\n",
      "  \"ocr_lang\": null,\n",
      "  \"processor_class\": \"LayoutLMv3Processor\",\n",
      "  \"resample\": 2,\n",
      "  \"size\": 224,\n",
      "  \"tesseract_config\": \"\"\n",
      "}\n",
      "\n",
      "loading file vocab.json from cache at /home/ubuntu/.cache/huggingface/hub/models--philschmid--lilt-en-funsd/snapshots/4c1f7f81f6fef1f9cc52b44da32a7899fab1bfc9/vocab.json\n",
      "loading file merges.txt from cache at /home/ubuntu/.cache/huggingface/hub/models--philschmid--lilt-en-funsd/snapshots/4c1f7f81f6fef1f9cc52b44da32a7899fab1bfc9/merges.txt\n",
      "loading file tokenizer.json from cache at /home/ubuntu/.cache/huggingface/hub/models--philschmid--lilt-en-funsd/snapshots/4c1f7f81f6fef1f9cc52b44da32a7899fab1bfc9/tokenizer.json\n",
      "loading file added_tokens.json from cache at None\n",
      "loading file special_tokens_map.json from cache at /home/ubuntu/.cache/huggingface/hub/models--philschmid--lilt-en-funsd/snapshots/4c1f7f81f6fef1f9cc52b44da32a7899fab1bfc9/special_tokens_map.json\n",
      "loading file tokenizer_config.json from cache at /home/ubuntu/.cache/huggingface/hub/models--philschmid--lilt-en-funsd/snapshots/4c1f7f81f6fef1f9cc52b44da32a7899fab1bfc9/tokenizer_config.json\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=780x1000>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import LiltForTokenClassification, LayoutLMv3Processor\n",
    "from PIL import Image, ImageDraw, ImageFont\n",
    "import torch\n",
    "\n",
    "# load model and processor from huggingface hub\n",
    "model = LiltForTokenClassification.from_pretrained(\"philschmid/lilt-en-funsd\")\n",
    "processor = LayoutLMv3Processor.from_pretrained(\"philschmid/lilt-en-funsd\")\n",
    "\n",
    "\n",
    "# helper function to unnormalize bboxes for drawing onto the image\n",
    "def unnormalize_box(bbox, width, height):\n",
    "    return [\n",
    "        width * (bbox[0] / 1000),\n",
    "        height * (bbox[1] / 1000),\n",
    "        width * (bbox[2] / 1000),\n",
    "        height * (bbox[3] / 1000),\n",
    "    ]\n",
    "\n",
    "\n",
    "label2color = {\n",
    "    \"B-HEADER\": \"blue\",\n",
    "    \"B-QUESTION\": \"red\",\n",
    "    \"B-ANSWER\": \"green\",\n",
    "    \"I-HEADER\": \"blue\",\n",
    "    \"I-QUESTION\": \"red\",\n",
    "    \"I-ANSWER\": \"green\",\n",
    "}\n",
    "# draw results onto the image\n",
    "def draw_boxes(image, boxes, predictions):\n",
    "    width, height = image.size\n",
    "    normalizes_boxes = [unnormalize_box(box, width, height) for box in boxes]\n",
    "\n",
    "    # draw predictions over the image\n",
    "    draw = ImageDraw.Draw(image)\n",
    "    font = ImageFont.load_default()\n",
    "    for prediction, box in zip(predictions, normalizes_boxes):\n",
    "        if prediction == \"O\":\n",
    "            continue\n",
    "        draw.rectangle(box, outline=\"black\")\n",
    "        draw.rectangle(box, outline=label2color[prediction])\n",
    "        draw.text((box[0] + 10, box[1] - 10), text=prediction, fill=label2color[prediction], font=font)\n",
    "    return image\n",
    "\n",
    "\n",
    "# run inference\n",
    "def run_inference(image, model=model, processor=processor, output_image=True):\n",
    "    # create model input\n",
    "    encoding = processor(image, return_tensors=\"pt\")\n",
    "    del encoding[\"pixel_values\"]\n",
    "    # run inference\n",
    "    outputs = model(**encoding)\n",
    "    predictions = outputs.logits.argmax(-1).squeeze().tolist()\n",
    "    # get labels\n",
    "    labels = [model.config.id2label[prediction] for prediction in predictions]\n",
    "    if output_image:\n",
    "        return draw_boxes(image, encoding[\"bbox\"][0], labels)\n",
    "    else:\n",
    "        return labels\n",
    "\n",
    "\n",
    "run_inference(dataset[\"test\"][34][\"image\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
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    "## Conclusion\n",
    "\n",
    "We managed to successfully fine-tune our LiLT model to extract information from forms. With only `149` training examples we achieved an overall f1 score of `0.89`, which is `12.66%` better than the original `LayoutLM` model (`0.79`). \n",
    "Additionally can LiLT be easily adapted to other languages, which makes it a great model for multilingual document understanding. \n",
    "\n",
    "Now its your time to integrate Transformers into your own projects. 🚀 "
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