Instructions to use mathieu1256/layoutlmv3-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mathieu1256/layoutlmv3-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mathieu1256/layoutlmv3-test")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("mathieu1256/layoutlmv3-test") model = AutoModelForTokenClassification.from_pretrained("mathieu1256/layoutlmv3-test") - Notebooks
- Google Colab
- Kaggle
layoutlmv3-test
This model is a fine-tuned version of microsoft/layoutlmv3-base on the cord dataset. It achieves the following results on the evaluation set:
- Loss: 1.3031
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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1
Training results
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2
- Downloads last month
- 4
Model tree for mathieu1256/layoutlmv3-test
Base model
microsoft/layoutlmv3-base