VesleAnne
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 4.9711
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: 20
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
5.3015 | 0.22 | 50 | 4.6180 |
4.4575 | 0.44 | 100 | 4.1929 |
4.1257 | 0.66 | 150 | 3.7698 |
3.8209 | 0.88 | 200 | 3.6617 |
3.55 | 1.11 | 250 | 3.3673 |
3.2001 | 1.33 | 300 | 3.1787 |
3.0574 | 1.55 | 350 | 2.9297 |
3.0092 | 1.77 | 400 | 2.6812 |
2.6884 | 1.99 | 450 | 3.1342 |
2.1791 | 2.21 | 500 | 2.8536 |
2.0669 | 2.43 | 550 | 2.4897 |
1.8517 | 2.65 | 600 | 2.5249 |
1.8483 | 2.88 | 650 | 2.2894 |
1.5113 | 3.1 | 700 | 2.1788 |
1.4535 | 3.32 | 750 | 2.0870 |
1.3109 | 3.54 | 800 | 2.5402 |
1.3217 | 3.76 | 850 | 2.1795 |
1.2264 | 3.98 | 900 | 2.2207 |
1.1269 | 4.2 | 950 | 2.7403 |
0.9924 | 4.42 | 1000 | 2.7255 |
1.0717 | 4.65 | 1050 | 3.0009 |
0.9322 | 4.87 | 1100 | 3.1246 |
0.9571 | 5.09 | 1150 | 3.4044 |
1.0463 | 5.31 | 1200 | 3.4533 |
0.8581 | 5.53 | 1250 | 3.0303 |
0.642 | 5.75 | 1300 | 3.6659 |
0.7991 | 5.97 | 1350 | 3.0716 |
0.4301 | 6.19 | 1400 | 3.3405 |
0.6194 | 6.42 | 1450 | 3.3413 |
0.7542 | 6.64 | 1500 | 3.2337 |
0.7794 | 6.86 | 1550 | 3.9620 |
0.8369 | 7.08 | 1600 | 3.2489 |
0.3225 | 7.3 | 1650 | 3.4928 |
0.4733 | 7.52 | 1700 | 3.3744 |
0.5242 | 7.74 | 1750 | 3.6537 |
0.3053 | 7.96 | 1800 | 3.9220 |
0.3376 | 8.19 | 1850 | 3.7916 |
0.6053 | 8.41 | 1900 | 3.3018 |
0.4441 | 8.63 | 1950 | 3.3969 |
0.3911 | 8.85 | 2000 | 3.3319 |
0.2184 | 9.07 | 2050 | 3.7348 |
0.2243 | 9.29 | 2100 | 3.6821 |
0.2049 | 9.51 | 2150 | 3.9148 |
0.1885 | 9.73 | 2200 | 4.1785 |
0.349 | 9.96 | 2250 | 3.9408 |
0.2324 | 10.18 | 2300 | 4.1387 |
0.2395 | 10.4 | 2350 | 4.0347 |
0.2178 | 10.62 | 2400 | 3.9753 |
0.1087 | 10.84 | 2450 | 4.4449 |
0.1677 | 11.06 | 2500 | 4.0552 |
0.1727 | 11.28 | 2550 | 3.9994 |
0.1876 | 11.5 | 2600 | 4.0458 |
0.165 | 11.73 | 2650 | 4.0139 |
0.1279 | 11.95 | 2700 | 4.2826 |
0.1795 | 12.17 | 2750 | 4.2433 |
0.1207 | 12.39 | 2800 | 4.3634 |
0.1421 | 12.61 | 2850 | 4.2781 |
0.1055 | 12.83 | 2900 | 4.3014 |
0.1568 | 13.05 | 2950 | 4.0862 |
0.0823 | 13.27 | 3000 | 4.4141 |
0.1443 | 13.5 | 3050 | 4.4451 |
0.0259 | 13.72 | 3100 | 4.6366 |
0.166 | 13.94 | 3150 | 4.4636 |
0.0451 | 14.16 | 3200 | 4.5297 |
0.0459 | 14.38 | 3250 | 4.6649 |
0.097 | 14.6 | 3300 | 4.6214 |
0.1233 | 14.82 | 3350 | 4.6148 |
0.1131 | 15.04 | 3400 | 4.4825 |
0.0897 | 15.27 | 3450 | 4.6712 |
0.0251 | 15.49 | 3500 | 4.9292 |
0.0814 | 15.71 | 3550 | 5.0073 |
0.0398 | 15.93 | 3600 | 4.9904 |
0.0165 | 16.15 | 3650 | 4.8615 |
0.0219 | 16.37 | 3700 | 5.0948 |
0.0606 | 16.59 | 3750 | 5.0635 |
0.1226 | 16.81 | 3800 | 5.0042 |
0.0577 | 17.04 | 3850 | 4.8606 |
0.0822 | 17.26 | 3900 | 4.8630 |
0.0288 | 17.48 | 3950 | 4.8891 |
0.0722 | 17.7 | 4000 | 4.8209 |
0.0339 | 17.92 | 4050 | 4.8838 |
0.0687 | 18.14 | 4100 | 4.9312 |
0.0083 | 18.36 | 4150 | 4.9128 |
0.02 | 18.58 | 4200 | 4.9771 |
0.0402 | 18.81 | 4250 | 4.9897 |
0.0274 | 19.03 | 4300 | 5.0073 |
0.0159 | 19.25 | 4350 | 5.0235 |
0.0363 | 19.47 | 4400 | 4.9699 |
0.0205 | 19.69 | 4450 | 4.9734 |
0.0213 | 19.91 | 4500 | 4.9711 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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