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--- |
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license: apache-2.0 |
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base_model: dandelin/vilt-b32-mlm |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- accuracy |
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model-index: |
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- name: vilt_finetuned_2 |
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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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# vilt_finetuned_2 |
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This model is a fine-tuned version of [dandelin/vilt-b32-mlm](https://huggingface.co/dandelin/vilt-b32-mlm) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.3663 |
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- F1: 0.6000 |
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- Roc Auc: 0.7866 |
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- Accuracy: 0.5735 |
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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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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
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| 44.1455 | 1.0 | 129 | 6.5479 | 0.1270 | 0.5367 | 0.0735 | |
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| 2.9608 | 2.0 | 258 | 2.7634 | 0.4385 | 0.6965 | 0.3934 | |
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| 2.3046 | 3.0 | 387 | 2.4919 | 0.4948 | 0.7204 | 0.4412 | |
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| 1.895 | 4.0 | 516 | 2.3418 | 0.5652 | 0.7627 | 0.5257 | |
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| 1.4785 | 5.0 | 645 | 2.6462 | 0.5720 | 0.7701 | 0.5404 | |
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| 1.1491 | 6.0 | 774 | 2.8805 | 0.6074 | 0.7884 | 0.5772 | |
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| 0.8297 | 7.0 | 903 | 3.1832 | 0.5977 | 0.7866 | 0.5735 | |
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| 0.7249 | 8.0 | 1032 | 3.2679 | 0.6054 | 0.7903 | 0.5809 | |
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| 2.1554 | 9.0 | 1161 | 3.2926 | 0.6119 | 0.7940 | 0.5846 | |
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| 0.5323 | 10.0 | 1290 | 3.3663 | 0.6000 | 0.7866 | 0.5735 | |
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### Framework versions |
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- Transformers 4.40.0.dev0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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