# Load model directly
from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering
processor = AutoProcessor.from_pretrained("mooner2/finetuned_docvqa")
model = AutoModelForDocumentQuestionAnswering.from_pretrained("mooner2/finetuned_docvqa")Quick Links
finetuned_docvqa
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
Training results
Framework versions
- Transformers 4.55.0
- Pytorch 2.5.0a0+b465a5843b.nv24.09
- Datasets 3.0.1
- Tokenizers 0.21.4
- Downloads last month
- 2
Model tree for mooner2/finetuned_docvqa
Base model
microsoft/layoutlmv2-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="mooner2/finetuned_docvqa")