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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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model-index: |
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- name: testing_img_token |
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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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# testing_img_token |
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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: 0.9187 |
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- Eading: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} |
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- Ext: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} |
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- Ub heading: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} |
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- Overall Precision: 0.0 |
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- Overall Recall: 0.0 |
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- Overall F1: 0.0 |
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- Overall Accuracy: 0.4848 |
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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: 0.0005 |
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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: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Eading | Ext | Ub heading | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:----------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 1.4635 | 1.43 | 10 | 1.2178 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | 0.0 | 0.0 | 0.0 | 0.4848 | |
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| 1.2206 | 2.86 | 20 | 1.0016 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | 0.0 | 0.0 | 0.0 | 0.4848 | |
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| 1.0569 | 4.29 | 30 | 0.9783 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | 0.0 | 0.0 | 0.0 | 0.4848 | |
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| 1.0201 | 5.71 | 40 | 0.9273 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | 0.0 | 0.0 | 0.0 | 0.4848 | |
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| 0.9888 | 7.14 | 50 | 0.9187 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | 0.0 | 0.0 | 0.0 | 0.4848 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.2 |
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