Instructions to use tiennvcs/layoutlmv2-base-uncased-finetuned-infovqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiennvcs/layoutlmv2-base-uncased-finetuned-infovqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="tiennvcs/layoutlmv2-base-uncased-finetuned-infovqa")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("tiennvcs/layoutlmv2-base-uncased-finetuned-infovqa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("tiennvcs/layoutlmv2-base-uncased-finetuned-infovqa") - Notebooks
- Google Colab
- Kaggle
| license: cc-by-sa-4.0 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: layoutlmv2-base-uncased-finetuned-infovqa | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # layoutlmv2-base-uncased-finetuned-infovqa | |
| This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.0870 | |
| ## 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: 4 | |
| - seed: 250500 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 3.8677 | 0.16 | 500 | 3.2829 | | |
| | 3.0395 | 0.33 | 1000 | 2.8431 | | |
| | 2.561 | 0.49 | 1500 | 2.5633 | | |
| | 2.41 | 0.65 | 2000 | 2.3548 | | |
| | 2.247 | 0.82 | 2500 | 2.2983 | | |
| | 2.1538 | 0.98 | 3000 | 2.2059 | | |
| | 1.7 | 1.14 | 3500 | 2.2006 | | |
| | 1.5705 | 1.31 | 4000 | 2.2736 | | |
| | 1.604 | 1.47 | 4500 | 2.1415 | | |
| | 1.5509 | 1.63 | 5000 | 2.0853 | | |
| | 1.5053 | 1.79 | 5500 | 2.1389 | | |
| | 1.4787 | 1.96 | 6000 | 2.0870 | | |
| ### Framework versions | |
| - Transformers 4.12.2 | |
| - Pytorch 1.8.0+cu101 | |
| - Datasets 1.14.0 | |
| - Tokenizers 0.10.3 | |