Instructions to use Hawk1234/layoutlmv2-base-uncased_finetuned_docvqa2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hawk1234/layoutlmv2-base-uncased_finetuned_docvqa2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="Hawk1234/layoutlmv2-base-uncased_finetuned_docvqa2")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("Hawk1234/layoutlmv2-base-uncased_finetuned_docvqa2") model = AutoModelForDocumentQuestionAnswering.from_pretrained("Hawk1234/layoutlmv2-base-uncased_finetuned_docvqa2") - Notebooks
- Google Colab
- Kaggle
layoutlmv2-base-uncased_finetuned_docvqa2
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: nan
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0 | 16.67 | 50 | nan |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for Hawk1234/layoutlmv2-base-uncased_finetuned_docvqa2
Base model
microsoft/layoutlmv2-base-uncased