Instructions to use Guigadal/layoutxlm-tokenclass-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Guigadal/layoutxlm-tokenclass-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Guigadal/layoutxlm-tokenclass-finetuned")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("Guigadal/layoutxlm-tokenclass-finetuned") model = AutoModelForTokenClassification.from_pretrained("Guigadal/layoutxlm-tokenclass-finetuned") - Notebooks
- Google Colab
- Kaggle
layoutxlm-tokenclass-finetuned
This model is a fine-tuned version of microsoft/layoutxlm-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2039
- Answer Precision: 0.9231
- Answer Recall: 0.9180
- Answer F1: 0.9205
- Answer Number: 366
- Header Precision: 0.8194
- Header Recall: 0.9219
- Header F1: 0.8676
- Header Number: 64
- Question Precision: 0.9115
- Question Recall: 0.9428
- Question F1: 0.9269
- Question Number: 437
- Overall Precision: 0.9088
- Overall Recall: 0.9308
- Overall F1: 0.9197
- Overall Accuracy: 0.9758
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
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: 5000
Training results
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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