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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: 1.1164 |
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- Answer: {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} |
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- Header: {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} |
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- Question: {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} |
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- Overall Precision: 0.8800 |
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- Overall Recall: 0.8892 |
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- Overall F1: 0.8846 |
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- Overall Accuracy: 0.8211 |
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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: 1 |
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- eval_batch_size: 1 |
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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: 1500 |
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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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| 0.0418 | 1.34 | 200 | 1.1164 | {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} | 0.8800 | 0.8892 | 0.8846 | 0.8211 | |
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| 0.0473 | 2.68 | 400 | 1.1164 | {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} | 0.8800 | 0.8892 | 0.8846 | 0.8211 | |
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| 0.0444 | 4.03 | 600 | 1.1164 | {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} | 0.8800 | 0.8892 | 0.8846 | 0.8211 | |
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| 0.0532 | 5.37 | 800 | 1.1164 | {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} | 0.8800 | 0.8892 | 0.8846 | 0.8211 | |
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| 0.0405 | 6.71 | 1000 | 1.1164 | {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} | 0.8800 | 0.8892 | 0.8846 | 0.8211 | |
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| 0.0383 | 8.05 | 1200 | 1.1164 | {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} | 0.8800 | 0.8892 | 0.8846 | 0.8211 | |
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| 0.0494 | 9.4 | 1400 | 1.1164 | {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} | 0.8800 | 0.8892 | 0.8846 | 0.8211 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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