Instructions to use Saed2023/layoutlmv3-finetuned-cord with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Saed2023/layoutlmv3-finetuned-cord with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Saed2023/layoutlmv3-finetuned-cord")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("Saed2023/layoutlmv3-finetuned-cord") model = AutoModelForTokenClassification.from_pretrained("Saed2023/layoutlmv3-finetuned-cord", device_map="auto") - Notebooks
- Google Colab
- Kaggle
layoutlmv3-finetuned-cord
This model is a fine-tuned version of Saed2023/layoutlmv3-finetuned-cord_100 on an unknown 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: 8
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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