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# layout_qa_hparam_tuning

This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3973

## 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-06
- train_batch_size: 5
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.0364        | 0.28  | 50   | 5.7109          |
| 5.6991        | 0.55  | 100  | 5.3444          |
| 5.3564        | 0.83  | 150  | 5.0481          |
| 5.1086        | 1.1   | 200  | 4.8591          |
| 4.8464        | 1.38  | 250  | 4.6824          |
| 4.7178        | 1.66  | 300  | 4.5995          |
| 4.6003        | 1.93  | 350  | 4.4761          |
| 4.4415        | 2.21  | 400  | 4.3781          |
| 4.3911        | 2.49  | 450  | 4.3017          |
| 4.2507        | 2.76  | 500  | 4.2496          |
| 4.1975        | 3.04  | 550  | 4.2142          |
| 4.0971        | 3.31  | 600  | 4.1524          |
| 4.0671        | 3.59  | 650  | 4.1038          |
| 4.0225        | 3.87  | 700  | 4.0486          |
| 3.9641        | 4.14  | 750  | 4.0478          |
| 3.9662        | 4.42  | 800  | 4.0082          |
| 3.8185        | 4.7   | 850  | 4.0001          |
| 3.8798        | 4.97  | 900  | 3.9235          |
| 3.7622        | 5.25  | 950  | 3.9549          |
| 3.7109        | 5.52  | 1000 | 3.8668          |
| 3.7218        | 5.8   | 1050 | 3.8849          |
| 3.6718        | 6.08  | 1100 | 3.9426          |
| 3.6925        | 6.35  | 1150 | 3.8288          |
| 3.5893        | 6.63  | 1200 | 3.8240          |
| 3.5545        | 6.91  | 1250 | 3.8149          |
| 3.4922        | 7.18  | 1300 | 3.8104          |
| 3.5117        | 7.46  | 1350 | 3.8128          |
| 3.3699        | 7.73  | 1400 | 3.7590          |
| 3.4538        | 8.01  | 1450 | 3.7577          |
| 3.3669        | 8.29  | 1500 | 3.7370          |
| 3.3516        | 8.56  | 1550 | 3.7278          |
| 3.3264        | 8.84  | 1600 | 3.6671          |
| 3.3102        | 9.12  | 1650 | 3.6953          |
| 3.241         | 9.39  | 1700 | 3.6474          |
| 3.278         | 9.67  | 1750 | 3.8793          |
| 3.2593        | 9.94  | 1800 | 3.6447          |
| 3.1663        | 10.22 | 1850 | 3.8442          |
| 3.0952        | 10.5  | 1900 | 3.6431          |
| 3.1355        | 10.77 | 1950 | 3.6261          |
| 3.0874        | 11.05 | 2000 | 3.5631          |
| 3.0178        | 11.33 | 2050 | 3.5662          |
| 2.9257        | 11.6  | 2100 | 3.4744          |
| 2.9164        | 11.88 | 2150 | 3.4374          |
| 2.8061        | 12.15 | 2200 | 3.4550          |
| 2.8664        | 12.43 | 2250 | 3.4217          |
| 2.7886        | 12.71 | 2300 | 3.4294          |
| 2.8398        | 12.98 | 2350 | 3.3906          |
| 2.7823        | 13.26 | 2400 | 3.4311          |
| 2.7024        | 13.54 | 2450 | 3.4267          |
| 2.7443        | 13.81 | 2500 | 3.3412          |
| 2.6747        | 14.09 | 2550 | 3.3656          |
| 2.723         | 14.36 | 2600 | 3.5019          |
| 2.6278        | 14.64 | 2650 | 3.4287          |
| 2.5001        | 14.92 | 2700 | 3.5152          |
| 2.5718        | 15.19 | 2750 | 3.3615          |
| 2.5734        | 15.47 | 2800 | 3.3193          |
| 2.5112        | 15.75 | 2850 | 3.4028          |
| 2.4499        | 16.02 | 2900 | 3.4374          |
| 2.4631        | 16.3  | 2950 | 3.3910          |
| 2.4246        | 16.57 | 3000 | 3.2926          |
| 2.4075        | 16.85 | 3050 | 3.1869          |
| 2.3691        | 17.13 | 3100 | 3.2002          |
| 2.3557        | 17.4  | 3150 | 3.1995          |
| 2.309         | 17.68 | 3200 | 3.3596          |
| 2.2738        | 17.96 | 3250 | 3.2819          |
| 2.2371        | 18.23 | 3300 | 3.2793          |
| 2.2578        | 18.51 | 3350 | 3.1955          |
| 2.1887        | 18.78 | 3400 | 3.1516          |
| 2.2166        | 19.06 | 3450 | 3.1920          |
| 2.1767        | 19.34 | 3500 | 3.0891          |
| 2.1307        | 19.61 | 3550 | 3.1467          |
| 2.1769        | 19.89 | 3600 | 3.1935          |
| 2.0798        | 20.17 | 3650 | 3.2426          |
| 2.1029        | 20.44 | 3700 | 3.1828          |
| 2.0654        | 20.72 | 3750 | 3.2298          |
| 1.997         | 20.99 | 3800 | 3.2313          |
| 1.9933        | 21.27 | 3850 | 3.1501          |
| 2.0084        | 21.55 | 3900 | 3.0830          |
| 1.9963        | 21.82 | 3950 | 3.2029          |
| 1.889         | 22.1  | 4000 | 3.2676          |
| 2.0014        | 22.38 | 4050 | 3.0189          |
| 1.9031        | 22.65 | 4100 | 3.0549          |
| 1.9464        | 22.93 | 4150 | 3.2659          |
| 1.8972        | 23.2  | 4200 | 3.2271          |
| 1.8549        | 23.48 | 4250 | 3.0585          |
| 1.8106        | 23.76 | 4300 | 3.2286          |
| 1.8222        | 24.03 | 4350 | 3.2233          |
| 1.8537        | 24.31 | 4400 | 2.9525          |
| 1.7717        | 24.59 | 4450 | 3.1129          |
| 1.8045        | 24.86 | 4500 | 3.1795          |
| 1.7783        | 25.14 | 4550 | 3.1206          |
| 1.7119        | 25.41 | 4600 | 3.1325          |
| 1.6936        | 25.69 | 4650 | 3.0850          |
| 1.776         | 25.97 | 4700 | 2.8785          |
| 1.7269        | 26.24 | 4750 | 2.9847          |
| 1.6276        | 26.52 | 4800 | 3.0743          |
| 1.6228        | 26.8  | 4850 | 3.1257          |
| 1.7509        | 27.07 | 4900 | 3.0451          |
| 1.6658        | 27.35 | 4950 | 3.1540          |
| 1.6688        | 27.62 | 5000 | 2.9553          |
| 1.5576        | 27.9  | 5050 | 3.0843          |
| 1.5457        | 28.18 | 5100 | 3.1677          |
| 1.638         | 28.45 | 5150 | 3.0357          |
| 1.5004        | 28.73 | 5200 | 3.0918          |
| 1.6639        | 29.01 | 5250 | 3.0215          |
| 1.5465        | 29.28 | 5300 | 3.1257          |
| 1.4719        | 29.56 | 5350 | 3.0513          |
| 1.5599        | 29.83 | 5400 | 3.0366          |
| 1.5755        | 30.11 | 5450 | 2.9535          |
| 1.496         | 30.39 | 5500 | 3.0343          |
| 1.5915        | 30.66 | 5550 | 3.1121          |
| 1.4198        | 30.94 | 5600 | 3.0673          |
| 1.5062        | 31.22 | 5650 | 2.9743          |
| 1.3817        | 31.49 | 5700 | 3.0471          |
| 1.4361        | 31.77 | 5750 | 2.9827          |
| 1.4624        | 32.04 | 5800 | 3.2212          |
| 1.4895        | 32.32 | 5850 | 3.0745          |
| 1.4598        | 32.6  | 5900 | 3.0424          |
| 1.4379        | 32.87 | 5950 | 3.0214          |
| 1.429         | 33.15 | 6000 | 3.9556          |
| 1.4837        | 33.43 | 6050 | 3.0527          |
| 1.4427        | 33.7  | 6100 | 3.0360          |
| 1.6037        | 33.98 | 6150 | 3.0011          |
| 1.3789        | 34.25 | 6200 | 2.9842          |
| 1.4559        | 34.53 | 6250 | 2.9825          |
| 1.3494        | 34.81 | 6300 | 3.0216          |
| 1.3313        | 35.08 | 6350 | 2.9506          |
| 1.3074        | 35.36 | 6400 | 2.9899          |
| 1.3534        | 35.64 | 6450 | 3.3824          |
| 1.4189        | 35.91 | 6500 | 2.9109          |
| 1.2795        | 36.19 | 6550 | 3.2013          |
| 1.377         | 36.46 | 6600 | 3.1894          |
| 1.3627        | 36.74 | 6650 | 3.0203          |
| 1.3731        | 37.02 | 6700 | 3.0597          |
| 1.2557        | 37.29 | 6750 | 3.1781          |
| 1.362         | 37.57 | 6800 | 3.3320          |
| 1.3448        | 37.85 | 6850 | 3.0893          |
| 1.3337        | 38.12 | 6900 | 3.3698          |
| 1.3455        | 38.4  | 6950 | 3.0614          |
| 1.3397        | 38.67 | 7000 | 3.2179          |
| 1.2439        | 38.95 | 7050 | 3.1908          |
| 1.25          | 39.23 | 7100 | 3.3292          |
| 1.3099        | 39.5  | 7150 | 3.1604          |
| 1.3465        | 39.78 | 7200 | 3.1365          |
| 1.2703        | 40.06 | 7250 | 3.2937          |
| 1.2662        | 40.33 | 7300 | 3.3199          |
| 1.233         | 40.61 | 7350 | 3.1995          |
| 1.2786        | 40.88 | 7400 | 3.1360          |
| 1.3409        | 41.16 | 7450 | 3.1513          |
| 1.2395        | 41.44 | 7500 | 3.2488          |
| 1.1858        | 41.71 | 7550 | 3.3637          |
| 1.3312        | 41.99 | 7600 | 3.2043          |
| 1.2245        | 42.27 | 7650 | 3.3381          |
| 1.2631        | 42.54 | 7700 | 3.3504          |
| 1.257         | 42.82 | 7750 | 3.1843          |
| 1.1715        | 43.09 | 7800 | 3.3320          |
| 1.2017        | 43.37 | 7850 | 3.1980          |
| 1.2711        | 43.65 | 7900 | 3.2528          |
| 1.2091        | 43.92 | 7950 | 3.1928          |
| 1.2574        | 44.2  | 8000 | 3.4765          |
| 1.1915        | 44.48 | 8050 | 3.2830          |
| 1.1754        | 44.75 | 8100 | 3.3196          |
| 1.263         | 45.03 | 8150 | 3.2323          |
| 1.1522        | 45.3  | 8200 | 3.2954          |
| 1.1563        | 45.58 | 8250 | 3.3078          |
| 1.2196        | 45.86 | 8300 | 3.4295          |
| 1.2375        | 46.13 | 8350 | 3.3431          |
| 1.2307        | 46.41 | 8400 | 3.3140          |
| 1.1926        | 46.69 | 8450 | 3.3558          |
| 1.1743        | 46.96 | 8500 | 3.2817          |
| 1.1721        | 47.24 | 8550 | 3.2732          |
| 1.192         | 47.51 | 8600 | 3.3022          |
| 1.1642        | 47.79 | 8650 | 3.3513          |
| 1.2049        | 48.07 | 8700 | 3.3494          |
| 1.1157        | 48.34 | 8750 | 3.3900          |
| 1.2006        | 48.62 | 8800 | 3.3109          |
| 1.1384        | 48.9  | 8850 | 3.3915          |
| 1.1437        | 49.17 | 8900 | 3.4193          |
| 1.2226        | 49.45 | 8950 | 3.3782          |
| 1.1074        | 49.72 | 9000 | 3.3965          |
| 1.1955        | 50.0  | 9050 | 3.3973          |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "base_model": "microsoft/layoutlmv2-base-uncased", "model-index": [{"name": "layout_qa_hparam_tuning", "results": []}]}
transformers tensorboard safetensors layoutlmv2 document-question-answering generated_from_trainer base_model:microsoft/layoutlmv2-base-uncased license:cc-by-nc-sa-4.0 endpoints_compatible region:us