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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- funsd-layoutlmv3 |
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model-index: |
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- name: lilt-en-funsd |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# lilt-en-funsd |
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This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8811 |
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- Answer: {'precision': 0.8316268486916951, 'recall': 0.8947368421052632, 'f1': 0.8620283018867924, 'number': 817} |
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- Header: {'precision': 0.4777777777777778, 'recall': 0.36134453781512604, 'f1': 0.41148325358851673, 'number': 119} |
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- Question: {'precision': 0.8480349344978166, 'recall': 0.9015784586815228, 'f1': 0.873987398739874, 'number': 1077} |
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- Overall Precision: 0.8254 |
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- Overall Recall: 0.8669 |
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- Overall F1: 0.8457 |
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- Overall Accuracy: 0.7806 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- training_steps: 200 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.7083 | 5.26 | 100 | 0.7032 | {'precision': 0.8124293785310734, 'recall': 0.8800489596083231, 'f1': 0.8448883666274971, 'number': 817} | {'precision': 0.4852941176470588, 'recall': 0.2773109243697479, 'f1': 0.35294117647058826, 'number': 119} | {'precision': 0.8360375747224594, 'recall': 0.9090064995357474, 'f1': 0.8709964412811388, 'number': 1077} | 0.8150 | 0.8599 | 0.8368 | 0.8089 | |
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| 0.1639 | 10.53 | 200 | 0.8811 | {'precision': 0.8316268486916951, 'recall': 0.8947368421052632, 'f1': 0.8620283018867924, 'number': 817} | {'precision': 0.4777777777777778, 'recall': 0.36134453781512604, 'f1': 0.41148325358851673, 'number': 119} | {'precision': 0.8480349344978166, 'recall': 0.9015784586815228, 'f1': 0.873987398739874, 'number': 1077} | 0.8254 | 0.8669 | 0.8457 | 0.7806 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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