# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("ThinhNP05/layoutlm-funsd")
model = AutoModelForTokenClassification.from_pretrained("ThinhNP05/layoutlm-funsd")Quick Links
layoutlm-funsd
This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the funsd 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for ThinhNP05/layoutlm-funsd
Base model
microsoft/layoutlm-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ThinhNP05/layoutlm-funsd")