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--- |
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license: mit |
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base_model: SCUT-DLVCLab/lilt-roberta-en-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: Data_extraction |
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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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# Data_extraction |
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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 None 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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- Ign: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} |
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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: 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: 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 | Ign | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:--------:|:----:|:---------------:|:------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.032 | 18.1818 | 200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 36.3636 | 400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 54.5455 | 600 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 72.7273 | 800 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 90.9091 | 1000 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 109.0909 | 1200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 127.2727 | 1400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 145.4545 | 1600 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 163.6364 | 1800 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 181.8182 | 2000 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 200.0 | 2200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0 | 218.1818 | 2400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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