Instructions to use speydach/layoutlmv2-finetuned-cord with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use speydach/layoutlmv2-finetuned-cord with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="speydach/layoutlmv2-finetuned-cord")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("speydach/layoutlmv2-finetuned-cord") model = AutoModelForTokenClassification.from_pretrained("speydach/layoutlmv2-finetuned-cord") - Notebooks
- Google Colab
- Kaggle
layoutlmv2-finetuned-cord
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 15
Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cpu
- Datasets 2.1.0
- Tokenizers 0.12.1
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