layoutlmv2-base-uncased_finetuned_docvqa
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.6030
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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
5.326 | 0.22 | 50 | 4.4949 |
4.292 | 0.44 | 100 | 3.9510 |
3.9419 | 0.66 | 150 | 3.9100 |
3.6895 | 0.88 | 200 | 3.5035 |
3.4052 | 1.11 | 250 | 3.4030 |
3.1405 | 1.33 | 300 | 3.2100 |
2.8966 | 1.55 | 350 | 2.9803 |
2.7874 | 1.77 | 400 | 2.7811 |
2.5385 | 1.99 | 450 | 2.4748 |
2.1532 | 2.21 | 500 | 2.5843 |
1.994 | 2.43 | 550 | 2.5459 |
1.8322 | 2.65 | 600 | 2.2316 |
1.7005 | 2.88 | 650 | 2.1888 |
1.4758 | 3.1 | 700 | 2.4578 |
1.3543 | 3.32 | 750 | 2.3368 |
1.1939 | 3.54 | 800 | 2.9737 |
1.294 | 3.76 | 850 | 2.4907 |
1.4519 | 3.98 | 900 | 1.9276 |
1.0517 | 4.2 | 950 | 2.9981 |
0.8171 | 4.42 | 1000 | 2.5618 |
1.0456 | 4.65 | 1050 | 2.3139 |
0.9222 | 4.87 | 1100 | 2.4243 |
0.758 | 5.09 | 1150 | 2.8167 |
0.7203 | 5.31 | 1200 | 2.9342 |
0.6748 | 5.53 | 1250 | 2.6396 |
0.6821 | 5.75 | 1300 | 2.5629 |
0.5898 | 5.97 | 1350 | 3.0276 |
0.3135 | 6.19 | 1400 | 3.2611 |
0.4407 | 6.42 | 1450 | 3.1793 |
0.5303 | 6.64 | 1500 | 3.0511 |
0.5294 | 6.86 | 1550 | 3.1106 |
0.3149 | 7.08 | 1600 | 3.2933 |
0.199 | 7.3 | 1650 | 3.4207 |
0.164 | 7.52 | 1700 | 3.4379 |
0.5258 | 7.74 | 1750 | 3.1339 |
0.336 | 7.96 | 1800 | 3.2394 |
0.3294 | 8.19 | 1850 | 3.0956 |
0.1587 | 8.41 | 1900 | 3.4282 |
0.2375 | 8.63 | 1950 | 3.3718 |
0.117 | 8.85 | 2000 | 3.5646 |
0.2873 | 9.07 | 2050 | 3.5213 |
0.2206 | 9.29 | 2100 | 3.5387 |
0.2503 | 9.51 | 2150 | 3.5683 |
0.0763 | 9.73 | 2200 | 3.6119 |
0.1344 | 9.96 | 2250 | 3.6030 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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