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--- |
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library_name: transformers |
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base_model: uitnlp/visobert |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: visobert-human-finetune-seed-69 |
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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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# visobert-human-finetune-seed-69 |
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This model is a fine-tuned version of [uitnlp/visobert](https://huggingface.co/uitnlp/visobert) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3651 |
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- Accuracy: 0.8720 |
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- Precision: 0.6884 |
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- Recall: 0.7116 |
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- F1: 0.6966 |
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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: 0.0001 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| No log | 1.0 | 346 | 0.3459 | 0.8656 | 0.6824 | 0.6750 | 0.6476 | |
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| 0.3399 | 2.0 | 692 | 0.3651 | 0.8720 | 0.6884 | 0.7116 | 0.6966 | |
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| 0.1883 | 3.0 | 1038 | 0.3686 | 0.8787 | 0.7074 | 0.6771 | 0.6913 | |
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| 0.1883 | 4.0 | 1384 | 0.5800 | 0.8720 | 0.7127 | 0.6519 | 0.6594 | |
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| 0.0913 | 5.0 | 1730 | 0.5507 | 0.8746 | 0.7029 | 0.6860 | 0.6934 | |
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| 0.0605 | 6.0 | 2076 | 0.6090 | 0.8757 | 0.7007 | 0.6792 | 0.6895 | |
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| 0.0605 | 7.0 | 2422 | 0.6178 | 0.8821 | 0.7406 | 0.6434 | 0.6830 | |
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### Framework versions |
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- Transformers 4.51.1 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.0 |
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