Instructions to use AnnasBlackHat/layoutlmv3-finetuned-cord_100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnnasBlackHat/layoutlmv3-finetuned-cord_100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AnnasBlackHat/layoutlmv3-finetuned-cord_100")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("AnnasBlackHat/layoutlmv3-finetuned-cord_100") model = AutoModelForTokenClassification.from_pretrained("AnnasBlackHat/layoutlmv3-finetuned-cord_100", device_map="auto") - Notebooks
- Google Colab
- Kaggle
layoutlmv3-finetuned-cord_100
This model is a fine-tuned version of microsoft/layoutlmv3-base on the cord 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: 1e-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
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.56 | 250 | 1.2530 | 0.6181 | 0.7171 | 0.6639 | 0.7402 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.11.0+cpu
- Datasets 2.2.2
- Tokenizers 0.12.1
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