--- tags: - generated_from_trainer - endpoints-template library_name: generic datasets: - funsd model-index: - name: layoutlm-funsd results: [] pipeline_tag: other --- # layoutlm-funsd This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset. It achieves the following results on the evaluation set: - Loss: 1.0045 - Answer: {'precision': 0.7348314606741573, 'recall': 0.8084054388133498, 'f1': 0.7698646262507357, 'number': 809} - Header: {'precision': 0.44285714285714284, 'recall': 0.5210084033613446, 'f1': 0.47876447876447875, 'number': 119} - Question: {'precision': 0.8211009174311926, 'recall': 0.8403755868544601, 'f1': 0.8306264501160092, 'number': 1065} - Overall Precision: 0.7599 - Overall Recall: 0.8083 - Overall F1: 0.7866 - Overall Accuracy: 0.8106 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ## Deploy Model with Inference Endpoints Before we can get started, make sure you meet all of the following requirements: 1. An Organization/User with an active plan and *WRITE* access to the model repository. 2. Can access the UI: [https://ui.endpoints.huggingface.co](https://ui.endpoints.huggingface.co/endpoints) ### 1. Deploy LayoutLM and Send requests In this tutorial, you will learn how to deploy a [LayoutLM](https://huggingface.co/docs/transformers/model_doc/layoutlm) to [Hugging Face Inference Endpoints](https://huggingface.co/inference-endpoints) and how you can integrate it via an API into your products. This tutorial is not covering how you create the custom handler for inference. If you want to learn how to create a custom Handler for Inference Endpoints, you can either checkout the [documentation](https://huggingface.co/docs/inference-endpoints/guides/custom_handler) or go through [“Custom Inference with Hugging Face Inference Endpoints”](https://www.philschmid.de/custom-inference-handler) We are going to deploy [philschmid/layoutlm-funsd](https://huggingface.co/philschmid/layoutlm-funsd) which implements the following `handler.py` ```python from typing import Dict, List, Any from transformers import LayoutLMForTokenClassification, LayoutLMv2Processor import torch from subprocess import run # install tesseract-ocr and pytesseract run("apt install -y tesseract-ocr", shell=True, check=True) run("pip install pytesseract", shell=True, check=True) # helper function to unnormalize bboxes for drawing onto the image def unnormalize_box(bbox, width, height): return [ width * (bbox[0] / 1000), height * (bbox[1] / 1000), width * (bbox[2] / 1000), height * (bbox[3] / 1000), ] # set device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") class EndpointHandler: def __init__(self, path=""): # load model and processor from path self.model = LayoutLMForTokenClassification.from_pretrained(path).to(device) self.processor = LayoutLMv2Processor.from_pretrained(path) def __call__(self, data: Dict[str, bytes]) -> Dict[str, List[Any]]: """ Args: data (:obj:): includes the deserialized image file as PIL.Image """ # process input image = data.pop("inputs", data) # process image encoding = self.processor(image, return_tensors="pt") # run prediction with torch.inference_mode(): outputs = self.model( input_ids=encoding.input_ids.to(device), bbox=encoding.bbox.to(device), attention_mask=encoding.attention_mask.to(device), token_type_ids=encoding.token_type_ids.to(device), ) predictions = outputs.logits.softmax(-1) # post process output result = [] for item, inp_ids, bbox in zip( predictions.squeeze(0).cpu(), encoding.input_ids.squeeze(0).cpu(), encoding.bbox.squeeze(0).cpu() ): label = self.model.config.id2label[int(item.argmax().cpu())] if label == "O": continue score = item.max().item() text = self.processor.tokenizer.decode(inp_ids) bbox = unnormalize_box(bbox.tolist(), image.width, image.height) result.append({"label": label, "score": score, "text": text, "bbox": bbox}) return {"predictions": result} ``` ### 2. Send HTTP request using Python Hugging Face Inference endpoints can directly work with binary data, this means that we can directly send our image from our document to the endpoint. We are going to use `requests` to send our requests. (make your you have it installed `pip install requests`) ```python import json import requests as r import mimetypes ENDPOINT_URL="" # url of your endpoint HF_TOKEN="" # organization token where you deployed your endpoint def predict(path_to_image:str=None): with open(path_to_image, "rb") as i: b = i.read() headers= { "Authorization": f"Bearer {HF_TOKEN}", "Content-Type": mimetypes.guess_type(path_to_image)[0] } response = r.post(ENDPOINT_URL, headers=headers, data=b) return response.json() prediction = predict(path_to_image="path_to_your_image.png") print(prediction) # {'predictions': [{'label': 'I-ANSWER', 'score': 0.4823932945728302, 'text': '[CLS]', 'bbox': [0.0, 0.0, 0.0, 0.0]}, {'label': 'B-HEADER', 'score': 0.992474377155304, 'text': 'your', 'bbox': [1712.529, 181.203, 1859.949, 228.88799999999998]}, ``` ### 3. Draw result on image To get a better understanding of what the model predicted you can also draw the predictions on the provided image. ```python from PIL import Image, ImageDraw, ImageFont # draw results on image def draw_result(path_to_image,result): image = Image.open(path_to_image) label2color = { "B-HEADER": "blue", "B-QUESTION": "red", "B-ANSWER": "green", "I-HEADER": "blue", "I-QUESTION": "red", "I-ANSWER": "green", } # draw predictions over the image draw = ImageDraw.Draw(image) font = ImageFont.load_default() for res in result: draw.rectangle(res["bbox"], outline="black") draw.rectangle(res["bbox"], outline=label2color[res["label"]]) draw.text((res["bbox"][0] + 10, res["bbox"][1] - 10), text=res["label"], fill=label2color[res["label"]], font=font) return image draw_result("path_to_your_image.png", prediction["predictions"]) ```