Instructions to use Chetan1997/layoutlmv2-finetuned-funsd-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chetan1997/layoutlmv2-finetuned-funsd-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Chetan1997/layoutlmv2-finetuned-funsd-test")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("Chetan1997/layoutlmv2-finetuned-funsd-test") model = AutoModelForTokenClassification.from_pretrained("Chetan1997/layoutlmv2-finetuned-funsd-test") - Notebooks
- Google Colab
- Kaggle
layoutlmv2-finetuned-funsd-test
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: 1000
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.8.0+cu101
- Datasets 2.2.2
- Tokenizers 0.12.1
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