|
--- |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: layoutlmv2-base-uncased_finetuned_docvqa |
|
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. --> |
|
|
|
# layoutlmv2-base-uncased_finetuned_docvqa |
|
|
|
This model was trained from scratch on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 4.6973 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:----:|:---------------:| |
|
| 5.3322 | 0.22 | 50 | 4.6214 | |
|
| 4.4006 | 0.44 | 100 | 4.1705 | |
|
| 3.9967 | 0.66 | 150 | 3.8115 | |
|
| 3.8349 | 0.88 | 200 | 3.5599 | |
|
| 3.4185 | 1.11 | 250 | 3.2811 | |
|
| 3.1129 | 1.33 | 300 | 3.3014 | |
|
| 2.9186 | 1.55 | 350 | 3.2583 | |
|
| 2.8937 | 1.77 | 400 | 2.6806 | |
|
| 2.5872 | 1.99 | 450 | 2.4617 | |
|
| 1.934 | 2.21 | 500 | 2.4098 | |
|
| 1.9086 | 2.43 | 550 | 2.3926 | |
|
| 1.7456 | 2.65 | 600 | 2.4048 | |
|
| 1.6092 | 2.88 | 650 | 2.2064 | |
|
| 1.5114 | 3.1 | 700 | 2.3882 | |
|
| 1.3176 | 3.32 | 750 | 2.2211 | |
|
| 1.2236 | 3.54 | 800 | 3.5792 | |
|
| 1.4123 | 3.76 | 850 | 2.0391 | |
|
| 1.2434 | 3.98 | 900 | 2.3563 | |
|
| 0.8727 | 4.2 | 950 | 2.6344 | |
|
| 0.9026 | 4.42 | 1000 | 3.1071 | |
|
| 1.0698 | 4.65 | 1050 | 3.1795 | |
|
| 0.9226 | 4.87 | 1100 | 3.0897 | |
|
| 1.0639 | 5.09 | 1150 | 3.2822 | |
|
| 0.7568 | 5.31 | 1200 | 3.0442 | |
|
| 0.8192 | 5.53 | 1250 | 2.8516 | |
|
| 0.5137 | 5.75 | 1300 | 3.1520 | |
|
| 0.7053 | 5.97 | 1350 | 2.7512 | |
|
| 0.4587 | 6.19 | 1400 | 2.8772 | |
|
| 0.4965 | 6.42 | 1450 | 3.1186 | |
|
| 0.4079 | 6.64 | 1500 | 3.0887 | |
|
| 0.6135 | 6.86 | 1550 | 3.0537 | |
|
| 0.7518 | 7.08 | 1600 | 3.2562 | |
|
| 0.336 | 7.3 | 1650 | 3.3709 | |
|
| 0.618 | 7.52 | 1700 | 2.5948 | |
|
| 0.3473 | 7.74 | 1750 | 3.1784 | |
|
| 0.3811 | 7.96 | 1800 | 3.3420 | |
|
| 0.2749 | 8.19 | 1850 | 3.5958 | |
|
| 0.2916 | 8.41 | 1900 | 3.7811 | |
|
| 0.2634 | 8.63 | 1950 | 3.8935 | |
|
| 0.4594 | 8.85 | 2000 | 3.4889 | |
|
| 0.2957 | 9.07 | 2050 | 3.5717 | |
|
| 0.1503 | 9.29 | 2100 | 3.8861 | |
|
| 0.2986 | 9.51 | 2150 | 3.8007 | |
|
| 0.3732 | 9.73 | 2200 | 3.5632 | |
|
| 0.282 | 9.96 | 2250 | 3.1815 | |
|
| 0.1781 | 10.18 | 2300 | 3.8386 | |
|
| 0.1678 | 10.4 | 2350 | 3.9624 | |
|
| 0.1813 | 10.62 | 2400 | 4.1113 | |
|
| 0.3601 | 10.84 | 2450 | 3.9760 | |
|
| 0.2374 | 11.06 | 2500 | 3.8321 | |
|
| 0.1251 | 11.28 | 2550 | 4.0945 | |
|
| 0.0741 | 11.5 | 2600 | 4.1810 | |
|
| 0.2846 | 11.73 | 2650 | 3.9240 | |
|
| 0.1964 | 11.95 | 2700 | 3.7833 | |
|
| 0.1413 | 12.17 | 2750 | 3.9830 | |
|
| 0.0696 | 12.39 | 2800 | 4.4270 | |
|
| 0.1401 | 12.61 | 2850 | 4.6113 | |
|
| 0.1267 | 12.83 | 2900 | 4.3713 | |
|
| 0.0689 | 13.05 | 2950 | 4.4582 | |
|
| 0.1636 | 13.27 | 3000 | 4.6710 | |
|
| 0.1935 | 13.5 | 3050 | 4.2833 | |
|
| 0.0936 | 13.72 | 3100 | 4.5357 | |
|
| 0.1001 | 13.94 | 3150 | 4.2660 | |
|
| 0.0509 | 14.16 | 3200 | 4.3863 | |
|
| 0.0193 | 14.38 | 3250 | 4.5554 | |
|
| 0.031 | 14.6 | 3300 | 4.4455 | |
|
| 0.0163 | 14.82 | 3350 | 4.4985 | |
|
| 0.1558 | 15.04 | 3400 | 4.5935 | |
|
| 0.0707 | 15.27 | 3450 | 4.4791 | |
|
| 0.0089 | 15.49 | 3500 | 4.6358 | |
|
| 0.0881 | 15.71 | 3550 | 4.5644 | |
|
| 0.1348 | 15.93 | 3600 | 4.5336 | |
|
| 0.0176 | 16.15 | 3650 | 4.5465 | |
|
| 0.0679 | 16.37 | 3700 | 4.4872 | |
|
| 0.0138 | 16.59 | 3750 | 4.3819 | |
|
| 0.0211 | 16.81 | 3800 | 4.5024 | |
|
| 0.0506 | 17.04 | 3850 | 4.4761 | |
|
| 0.0051 | 17.26 | 3900 | 4.5898 | |
|
| 0.075 | 17.48 | 3950 | 4.5427 | |
|
| 0.0089 | 17.7 | 4000 | 4.6244 | |
|
| 0.0298 | 17.92 | 4050 | 4.6440 | |
|
| 0.0034 | 18.14 | 4100 | 4.6486 | |
|
| 0.0594 | 18.36 | 4150 | 4.6438 | |
|
| 0.0179 | 18.58 | 4200 | 4.6552 | |
|
| 0.0081 | 18.81 | 4250 | 4.6508 | |
|
| 0.0046 | 19.03 | 4300 | 4.6808 | |
|
| 0.0618 | 19.25 | 4350 | 4.6856 | |
|
| 0.0033 | 19.47 | 4400 | 4.6763 | |
|
| 0.0062 | 19.69 | 4450 | 4.6892 | |
|
| 0.0404 | 19.91 | 4500 | 4.6973 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.30.2 |
|
- Pytorch 1.10.0+cu113 |
|
- Datasets 2.13.2 |
|
- Tokenizers 0.13.3 |
|
|
