Vilt B32 Finetuned Vqa Willowmore
This model is a fine-tuned version of dandelin/vilt-b32-finetuned-vqa on an unknown dataset. It achieves the following results on the evaluation set: Loss: 0.2704
Fonte do modelo
- Tipo
- Modelo
- Tamanho do arquivo
- 430 MB
- Modelo base
- dandelin/vilt-b32-finetuned-vqa
- Versão
- vRevision 94af877
- Atualizado
- 30 de out. de 2026
- Downloads
- 18
- Fonte
- Hugging Face
Descrição da fonte
vilt-b32-finetuned-vqa-willowmore
This model is a fine-tuned version of dandelin/vilt-b32-finetuned-vqa on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2704
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.8442 | 1.0 | 74 | 0.3420 |
| 0.4009 | 2.0 | 148 | 0.2322 |
| 0.2016 | 3.0 | 222 | 0.0716 |
| 0.0257 | 4.0 | 296 | 0.4162 |
| 0.0082 | 5.0 | 370 | 0.0805 |
| 0.0022 | 6.0 | 444 | 0.0889 |
| 0.0013 | 7.0 | 518 | 0.1099 |
| 0.0011 | 8.0 | 592 | 0.1282 |
| 0.0008 | 9.0 | 666 | 0.1389 |
| 0.0007 | 10.0 | 740 | 0.1484 |
| 0.0006 | 11.0 | 814 | 0.1567 |
| 0.0005 | 12.0 | 888 | 0.1632 |
| 0.0004 | 13.0 | 962 | 0.1687 |
| 0.0004 | 14.0 | 1036 | 0.1735 |
| 0.0003 | 15.0 | 1110 | 0.1785 |
| 0.0003 | 16.0 | 1184 | 0.1818 |
| 0.0003 | 17.0 | 1258 | 0.1855 |
| 0.0002 | 18.0 | 1332 | 0.1888 |
| 0.0002 | 19.0 | 1406 | 0.1917 |
| 0.0002 | 20.0 | 1480 | 0.1947 |
| 0.0002 | 21.0 | 1554 | 0.1975 |
| 0.0002 | 22.0 | 1628 | 0.2001 |
| 0.0002 | 23.0 | 1702 | 0.2022 |
| 0.0002 | 24.0 | 1776 | 0.2045 |
| 0.0001 | 25.0 | 1850 | 0.2066 |
| 0.0001 | 26.0 | 1924 | 0.2085 |
| 0.0001 | 27.0 | 1998 | 0.2105 |
| 0.0001 | 28.0 | 2072 | 0.2124 |
| 0.0001 | 29.0 | 2146 | 0.2141 |
| 0.0001 | 30.0 | 2220 | 0.2158 |
| 0.0001 | 31.0 | 2294 | 0.2175 |
| 0.0001 | 32.0 | 2368 | 0.2190 |
| 0.0001 | 33.0 | 2442 | 0.2203 |
| 0.0001 | 34.0 | 2516 | 0.2218 |
| 0.0001 | 35.0 | 2590 | 0.2234 |
| 0.0001 | 36.0 | 2664 | 0.2247 |
| 0.0001 | 37.0 | 2738 | 0.2261 |
| 0.0001 | 38.0 | 2812 | 0.2274 |
| 0.0001 | 39.0 | 2886 | 0.2287 |
| 0.0001 | 40.0 | 2960 | 0.2300 |
| 0.0001 | 41.0 | 3034 | 0.2312 |
| 0.0001 | 42.0 | 3108 | 0.2324 |
| 0.0001 | 43.0 | 3182 | 0.2337 |
| 0.0 | 44.0 | 3256 | 0.2348 |
| 0.0 | 45.0 | 3330 | 0.2359 |
| 0.0 | 46.0 | 3404 | 0.2371 |
| 0.0 | 47.0 | 3478 | 0.2382 |
| 0.0 | 48.0 | 3552 | 0.2393 |
| 0.0 | 49.0 | 3626 | 0.2404 |
| 0.0 | 50.0 | 3700 | 0.2414 |
| 0.0 | 51.0 | 3774 | 0.2424 |
| 0.0 | 52.0 | 3848 | 0.2434 |
| 0.0 | 53.0 | 3922 | 0.2444 |
| 0.0 | 54.0 | 3996 | 0.2454 |
| 0.0 | 55.0 | 4070 | 0.2463 |
| 0.0 | 56.0 | 4144 | 0.2473 |
| 0.0 | 57.0 | 4218 | 0.2482 |
| 0.0 | 58.0 | 4292 | 0.2491 |
| 0.0 | 59.0 | 4366 | 0.2500 |
| 0.0 | 60.0 | 4440 | 0.2509 |
| 0.0 | 61.0 | 4514 | 0.2517 |
| 0.0 | 62.0 | 4588 | 0.2525 |
| 0.0 | 63.0 | 4662 | 0.2533 |
| 0.0 | 64.0 | 4736 | 0.2541 |
| 0.0 | 65.0 | 4810 | 0.2549 |
| 0.0 | 66.0 | 4884 | 0.2556 |
| 0.0 | 67.0 | 4958 | 0.2564 |
| 0.0 | 68.0 | 5032 | 0.2571 |
| 0.0 | 69.0 | 5106 | 0.2578 |
| 0.0 | 70.0 | 5180 | 0.2585 |
| 0.0 | 71.0 | 5254 | 0.2592 |
| 0.0 | 72.0 | 5328 | 0.2598 |
| 0.0 | 73.0 | 5402 | 0.2605 |
| 0.0 | 74.0 | 5476 | 0.2611 |
| 0.0 | 75.0 | 5550 | 0.2617 |
| 0.0 | 76.0 | 5624 | 0.2623 |
| 0.0 | 77.0 | 5698 | 0.2629 |
| 0.0 | 78.0 | 5772 | 0.2634 |
| 0.0 | 79.0 | 5846 | 0.2640 |
| 0.0 | 80.0 | 5920 | 0.2645 |
| 0.0 | 81.0 | 5994 | 0.2650 |
| 0.0 | 82.0 | 6068 | 0.2655 |
| 0.0 | 83.0 | 6142 | 0.2660 |
| 0.0 | 84.0 | 6216 | 0.2664 |
| 0.0 | 85.0 | 6290 | 0.2669 |
| 0.0 | 86.0 | 6364 | 0.2673 |
| 0.0 | 87.0 | 6438 | 0.2677 |
| 0.0 | 88.0 | 6512 | 0.2680 |
| 0.0 | 89.0 | 6586 | 0.2684 |
| 0.0 | 90.0 | 6660 | 0.2687 |
| 0.0 | 91.0 | 6734 | 0.2690 |
| 0.0 | 92.0 | 6808 | 0.2693 |
| 0.0 | 93.0 | 6882 | 0.2695 |
| 0.0 | 94.0 | 6956 | 0.2697 |
| 0.0 | 95.0 | 7030 | 0.2699 |
| 0.0 | 96.0 | 7104 | 0.2701 |
| 0.0 | 97.0 | 7178 | 0.2702 |
| 0.0 | 98.0 | 7252 | 0.2703 |
| 0.0 | 99.0 | 7326 | 0.2704 |
| 0.0 | 100.0 | 7400 | 0.2704 |
Framework versions
- Transformers 4.48.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
Fontes
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Fontes
1 fonteVerificado 30 de out.
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Artefatos de modelo
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safetensors · FP32 (tensor header) · 430 MB · SHA-256 54a00c57fd71…950e · Hugging Face
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3 trechos--- library_name: transformers license: apache-2.0 base_model: dandelin/vilt-b32-finetuned-vqa tags: - generated_from_trainer model-index: - name: vilt-b32-finetuned-vqa-willowmore results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vilt-b32-finetuned-vqa-willowmore This model is a fine-tuned version of [dandelin/vilt-b32-finetuned-vqa](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2704 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.8442 | 1.0 | 74 | 0.3420 | | 0.4009 | 2.0 | 148 | 0.2322 | | 0.2016 | 3.0 | 222 | 0.0716 | | 0.0257 | 4.0 | 296 | 0.4162 | | 0.0082 | 5.0 | 370 | 0.0805 | | 0.0022 | 6.0 | 444 | 0.0889 | | 0.0013 | 7.0 | 518 | 0.1099 | | 0.0011 | 8.0 | 592 | 0.1282 | | 0.0008 | 9.0 | 666 | 0.1389 | | 0.0007 | 10.0 | 740 | 0.1484 | | 0.0006 | 11.0 | 814 | 0.1567 | | 0.0005 | 12.0 | 888 | 0.1632 | | 0.0004 | 13.0 | 962 | 0.1687 | | 0.0004 | 14.0 | 1036 | 0.1735 | | 0.0003 | 15.0 | 1110 | 0.1785 | | 0.0003 | 16.0 | 1184 | 0.1818 | | 0.0003 | 17.0 | 1258 | 0.1855 | | 0.0002 | 18.0 | 1332 | 0.1888 | | 0.0002 | 19.0 | 1406 | 0.1917 | | 0.0002 | 20.0 | 1480 | 0.1947 | | 0.0002 | 21.0 | 1554 | 0.1975 | | 0.0002 | 22.0 | 1628 | 0.2001 | | 0.0002 | 23.0 | 1702 | 0.2022 | | 0.0002 | 24.0 | 1776 | 0.2045 | | 0.0001 | 25.0 | 1850 | 0.2066 | | 0.0001 | 26.0 | 1924 | 0.2085 | | 0.0001 | 27.0 | 1998 | 0.2105 | | 0.0001 | 28.0 | 2072 | 0.2124 | | 0.0001 | 29.0 | 2146 | 0.2141 | | 0.0001 | 30.0 | 2220 | 0.2158 | | 0.0001 | 31.0 | 2294 | 0.2175 | | 0.0001 | 32.0 | 2368 | 0.2190 | | 0.0001 | 33.0 | 2442 | 0.2203 | | 0.00...
Source context: 18 downloads · 0 likes · Pipeline visual-question-answering · Library transformers · Repo muhammadfhadli/vilt-b32-finetuned-vqa-willowmore
Source context: 7 downloads · 0 likes · Pipeline visual-question-answering · Library transformers · Repo muhammadfhadli/vilt-b32-finetuned-vqa-willowmore
