bert-large-uncased-finetuned-vi-infovqa
This model is a fine-tuned version of bert-large-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 7.4878
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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 250500
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.11 | 100 | 4.6256 |
| No log | 0.21 | 200 | 4.4042 |
| No log | 0.32 | 300 | 5.0021 |
| No log | 0.43 | 400 | 4.2825 |
| 4.6758 | 0.53 | 500 | 4.3886 |
| 4.6758 | 0.64 | 600 | 4.2519 |
| 4.6758 | 0.75 | 700 | 4.2977 |
| 4.6758 | 0.85 | 800 | 3.9916 |
| 4.6758 | 0.96 | 900 | 4.1650 |
| 4.1715 | 1.07 | 1000 | 4.5001 |
| 4.1715 | 1.17 | 1100 | 4.0898 |
| 4.1715 | 1.28 | 1200 | 4.1623 |
| 4.1715 | 1.39 | 1300 | 4.3271 |
| 4.1715 | 1.49 | 1400 | 3.9661 |
| 3.7926 | 1.6 | 1500 | 3.8727 |
| 3.7926 | 1.71 | 1600 | 3.8934 |
| 3.7926 | 1.81 | 1700 | 3.7262 |
| 3.7926 | 1.92 | 1800 | 3.7701 |
| 3.7926 | 2.03 | 1900 | 3.7653 |
| 3.5041 | 2.13 | 2000 | 3.9261 |
| 3.5041 | 2.24 | 2100 | 4.0915 |
| 3.5041 | 2.35 | 2200 | 4.0348 |
| 3.5041 | 2.45 | 2300 | 4.0212 |
| 3.5041 | 2.56 | 2400 | 4.4653 |
| 2.8475 | 2.67 | 2500 | 4.2959 |
| 2.8475 | 2.77 | 2600 | 4.1039 |
| 2.8475 | 2.88 | 2700 | 3.8037 |
| 2.8475 | 2.99 | 2800 | 3.7552 |
| 2.8475 | 3.09 | 2900 | 4.2476 |
| 2.5488 | 3.2 | 3000 | 4.6716 |
| 2.5488 | 3.3 | 3100 | 4.7058 |
| 2.5488 | 3.41 | 3200 | 4.6266 |
| 2.5488 | 3.52 | 3300 | 4.5697 |
| 2.5488 | 3.62 | 3400 | 5.1017 |
| 2.0347 | 3.73 | 3500 | 4.6254 |
| 2.0347 | 3.84 | 3600 | 4.4822 |
| 2.0347 | 3.94 | 3700 | 4.9413 |
| 2.0347 | 4.05 | 3800 | 5.3600 |
| 2.0347 | 4.16 | 3900 | 5.7323 |
| 1.6566 | 4.26 | 4000 | 5.8822 |
| 1.6566 | 4.37 | 4100 | 6.0173 |
| 1.6566 | 4.48 | 4200 | 5.6688 |
| 1.6566 | 4.58 | 4300 | 6.0617 |
| 1.6566 | 4.69 | 4400 | 6.6631 |
| 1.3348 | 4.8 | 4500 | 6.0290 |
| 1.3348 | 4.9 | 4600 | 6.2455 |
| 1.3348 | 5.01 | 4700 | 6.0963 |
| 1.3348 | 5.12 | 4800 | 7.0983 |
| 1.3348 | 5.22 | 4900 | 7.5483 |
| 1.0701 | 5.33 | 5000 | 7.7187 |
| 1.0701 | 5.44 | 5100 | 7.4630 |
| 1.0701 | 5.54 | 5200 | 7.1394 |
| 1.0701 | 5.65 | 5300 | 7.0703 |
| 1.0701 | 5.76 | 5400 | 7.5611 |
| 0.9414 | 5.86 | 5500 | 7.6038 |
| 0.9414 | 5.97 | 5600 | 7.4878 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
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