Instructions to use ahmedshark/layoutlmv2-base-uncased_finetuned_docvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahmedshark/layoutlmv2-base-uncased_finetuned_docvqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="ahmedshark/layoutlmv2-base-uncased_finetuned_docvqa")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("ahmedshark/layoutlmv2-base-uncased_finetuned_docvqa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("ahmedshark/layoutlmv2-base-uncased_finetuned_docvqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
layoutlmv2-base-uncased_finetuned_docvqa
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: 5.7397
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 6.1869 | 1.0 | 3 | 5.9733 |
| 5.9493 | 2.0 | 6 | 5.8083 |
| 5.8049 | 3.0 | 9 | 5.7397 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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Model tree for ahmedshark/layoutlmv2-base-uncased_finetuned_docvqa
Base model
microsoft/layoutlmv2-base-uncased