Instructions to use tiennvcs/layoutlmv2-base-uncased-finetuned-vi-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-vi-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-vi-infovqa")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("tiennvcs/layoutlmv2-base-uncased-finetuned-vi-infovqa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("tiennvcs/layoutlmv2-base-uncased-finetuned-vi-infovqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
layoutlmv2-base-uncased-finetuned-vi-infovqa
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: 4.3332
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 |
|---|---|---|---|
| No log | 0.33 | 100 | 5.3461 |
| No log | 0.66 | 200 | 4.9734 |
| No log | 0.99 | 300 | 4.6074 |
| No log | 1.32 | 400 | 4.4548 |
| 4.6355 | 1.65 | 500 | 4.3831 |
| 4.6355 | 1.98 | 600 | 4.3332 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.8.0+cu101
- Datasets 1.17.0
- Tokenizers 0.10.3
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