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)
This model can be used for NER on French scanned documents. It can recognize 4 categories: "question", "answer", "header" and "other".
This checkpoint used the French portion of the multilingual XFUND dataset.
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
- Transformers 4.22.1
- Pytorch 1.10.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
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Inference API has been turned off for this model.