# Load model directly
from transformers import AutoProcessor, AutoModelForTokenClassification
processor = AutoProcessor.from_pretrained("nhung/layoutxlm-de-durch")
model = AutoModelForTokenClassification.from_pretrained("nhung/layoutxlm-de-durch")Quick Links
layoutxlm-de-durch
This model is a fine-tuned version of microsoft/layoutxlm-base on the xfun dataset.
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: 6.25e-06
- 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: 15000
Training results
Framework versions
- Transformers 4.27.4
- Pytorch 1.10.0+cu111
- Datasets 2.11.0
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="nhung/layoutxlm-de-durch")