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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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- mydata |
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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 mydata dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0000 |
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- In: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} |
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- Ear: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} |
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- Overall Precision: 1.0 |
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- Overall Recall: 1.0 |
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- Overall F1: 1.0 |
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- Overall Accuracy: 1.0 |
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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: 2 |
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- eval_batch_size: 2 |
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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: 2500 |
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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 | In | Ear | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.017 | 66.67 | 200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 133.33 | 400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 200.0 | 600 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 266.67 | 800 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 333.33 | 1000 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 400.0 | 1200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 466.67 | 1400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 533.33 | 1600 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 600.0 | 1800 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 666.67 | 2000 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 733.33 | 2200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 800.0 | 2400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6} | 1.0 | 1.0 | 1.0 | 1.0 | |
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### Framework versions |
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- Transformers 4.30.0.dev0 |
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- Pytorch 1.8.0+cu101 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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