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