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lilt-en-funsd
This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set:
- Loss: 0.6139
- Answer: {'precision': 0.8076923076923077, 'recall': 0.8739290085679314, 'f1': 0.8395061728395062, 'number': 817}
- Header: {'precision': 0.3148148148148148, 'recall': 0.42857142857142855, 'f1': 0.36298932384341637, 'number': 119}
- Question: {'precision': 0.796595744680851, 'recall': 0.8690807799442897, 'f1': 0.8312611012433393, 'number': 1077}
- Overall Precision: 0.7659
- Overall Recall: 0.8450
- Overall F1: 0.8035
- Overall Accuracy: 0.8009
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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 260
Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.7536 | 2.67 | 200 | 0.6139 | {'precision': 0.8076923076923077, 'recall': 0.8739290085679314, 'f1': 0.8395061728395062, 'number': 817} | {'precision': 0.3148148148148148, 'recall': 0.42857142857142855, 'f1': 0.36298932384341637, 'number': 119} | {'precision': 0.796595744680851, 'recall': 0.8690807799442897, 'f1': 0.8312611012433393, 'number': 1077} | 0.7659 | 0.8450 | 0.8035 | 0.8009 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
