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--- |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: VIT_Captioning |
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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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# VIT_Captioning |
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.0461 |
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- Rouge1: 0.4850 |
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- Rouge2: 0.2566 |
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- Rougel: 0.3589 |
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- Rougelsum: 0.3595 |
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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: Use 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: 1024 |
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- num_epochs: 15 |
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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 | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:| |
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| 2.4042 | 1.0 | 1828 | 1.7451 | 0.4622 | 0.1906 | 0.3370 | 0.3422 | |
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| 1.5875 | 2.0 | 3656 | 1.5933 | 0.4599 | 0.2060 | 0.3451 | 0.3472 | |
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| 1.3882 | 3.0 | 5484 | 1.5322 | 0.4606 | 0.2082 | 0.3422 | 0.3442 | |
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| 1.2415 | 4.0 | 7312 | 1.5130 | 0.4687 | 0.2208 | 0.3458 | 0.3476 | |
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| 1.1113 | 5.0 | 9140 | 1.5186 | 0.4630 | 0.2146 | 0.3398 | 0.3402 | |
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| 0.9671 | 6.0 | 10968 | 1.5683 | 0.4720 | 0.2290 | 0.3517 | 0.3520 | |
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| 0.8528 | 7.0 | 12796 | 1.6352 | 0.4704 | 0.2281 | 0.3491 | 0.3496 | |
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| 0.7555 | 8.0 | 14624 | 1.7122 | 0.4725 | 0.2305 | 0.3477 | 0.3481 | |
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| 0.6567 | 9.0 | 16452 | 1.7814 | 0.4763 | 0.2389 | 0.3537 | 0.3543 | |
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| 0.5612 | 10.0 | 18280 | 1.8528 | 0.4777 | 0.2410 | 0.3515 | 0.3516 | |
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| 0.4953 | 11.0 | 20108 | 1.9072 | 0.4799 | 0.2487 | 0.3562 | 0.3565 | |
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| 0.4445 | 12.0 | 21936 | 1.9503 | 0.4829 | 0.2514 | 0.3571 | 0.3574 | |
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| 0.3976 | 13.0 | 23764 | 1.9928 | 0.4834 | 0.2543 | 0.3569 | 0.3573 | |
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| 0.3643 | 14.0 | 25592 | 2.0249 | 0.4820 | 0.2520 | 0.3575 | 0.3581 | |
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| 0.3263 | 15.0 | 27420 | 2.0461 | 0.4850 | 0.2566 | 0.3589 | 0.3595 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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