Instructions to use zibajoon/20231114_DocVQA_laytlm_2ep_full50k_Doc_NA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zibajoon/20231114_DocVQA_laytlm_2ep_full50k_Doc_NA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="zibajoon/20231114_DocVQA_laytlm_2ep_full50k_Doc_NA")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("zibajoon/20231114_DocVQA_laytlm_2ep_full50k_Doc_NA") model = AutoModelForDocumentQuestionAnswering.from_pretrained("zibajoon/20231114_DocVQA_laytlm_2ep_full50k_Doc_NA") - Notebooks
- Google Colab
- Kaggle
20231114_DocVQA_layoutlm2_full_dataset_50k_5_epochs
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: 0.6737
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.7512 | 1.0 | 1261 | 0.8101 |
| 0.5314 | 2.0 | 2522 | 0.6737 |
Framework versions
- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for zibajoon/20231114_DocVQA_laytlm_2ep_full50k_Doc_NA
Base model
microsoft/layoutlmv2-base-uncased