readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - nielsr/XFUN
  model-index:
  - name: layoutxlm-finetuned-xfund-fr
    results: []
  inference: false
  ---
  
  # layoutxlm-finetuned-xfund-fr
  
  This model is a fine-tuned version of [microsoft/layoutxlm-base](https://huggingface.co/microsoft/layoutxlm-base) on the [XFUND](https://github.com/doc-analysis/XFUND) dataset (French split).
  
  ## Model usage
  
  Note that this model requires Tesseract, French package, in order to perform inference. You can install it using `!sudo apt-get install tesseract-ocr-fra`.
  
  Here's how to use this model:
  
  ```
  from transformers import AutoProcessor, AutoModelForTokenClassification
  import torch
  from PIL import Image
  
  processor = AutoProcessor.from_pretrained("nielsr/layoutxlm-finetuned-xfund-fr")
  model = AutoModelForTokenClassification.from_pretrained(nielsr/layoutxlm-finetuned-xfund-fr")
  
  # assuming you have a French document, turned into an image
  image = Image("...").convert("RGB")
  
  # prepare for the model
  encoding = processor(image, return_offsets_mapping=True, padding="max_length", max_length=512, truncation=True, return_tensors="pt")
  
  with torch.no_grad():
    outputs = model(**encoding)
    logits = outputs.logits
    
  predictions = logits.argmax(-1)
  ```
  
  ## Intended uses & limitations
  
  This model can be used for NER on French scanned documents. It can recognize 4 categories: "question", "answer", "header" and "other".
  
  ## Training and evaluation data
  
  This checkpoint used the French portion of the multilingual [XFUND](https://github.com/doc-analysis/XFUND) dataset.
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 1e-05
  - train_batch_size: 2
  - eval_batch_size: 2
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - training_steps: 1000
  
  ### Training results
  
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.10.0+cu111
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  