Instructions to use IBoH/cord-layoutlmv3-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IBoH/cord-layoutlmv3-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="IBoH/cord-layoutlmv3-finetuned")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("IBoH/cord-layoutlmv3-finetuned") model = AutoModelForTokenClassification.from_pretrained("IBoH/cord-layoutlmv3-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
cord-layoutlmv3-finetuned
This model is a fine-tuned version of microsoft/layoutlmv3-base on the None dataset.
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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 0.62 | 100 | 0.9126 | 0.6542 | 0.6801 | 0.6669 | 0.7612 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.11.0
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