File size: 21,627 Bytes
b85ef44 7b686d4 b85ef44 7b686d4 b85ef44 8b7b797 b85ef44 7b686d4 72b8226 b85ef44 7b686d4 8b7b797 72b8226 8b7b797 72b8226 8b7b797 b85ef44 72b8226 b85ef44 8b7b797 b85ef44 8b7b797 b85ef44 8b7b797 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 |
---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-large
tags:
- generated_from_trainer
datasets:
- openfoodfacts/nutrient-detection-layout
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nutrition-extractor
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: openfoodfacts/nutrient-detection-layout
type: openfoodfacts/nutrient-detection-layout
metrics:
- name: Precision
type: precision
value: 0.9545036764705882
- name: Recall
type: recall
value: 0.9647004180213655
- name: F1
type: f1
value: 0.9595749595749595
- name: Accuracy
type: accuracy
value: 0.9916725247390905
---
# nutrition-extractor
This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on the openfoodfacts/nutrient-detection-layout dataset.
It allows to automatically extract nutrition values from images of nutrition tables.
It achieves the following results on the evaluation set:
- Loss: 0.0534
- Precision: 0.9545
- Recall: 0.9647
- F1: 0.9596
- Accuracy: 0.9917
## Model description
This model can extract nutrient values from nutrition tables. This was developped as part of the Nutrisight project.
For more information about the project, please refer to the [nutrisight](https://github.com/openfoodfacts/openfoodfacts-ai/tree/develop/nutrisight) directory in the openfoodfacts-ai GitHub repository.
As any model using the LayoutLM architecture, this model expects as input:
- the image
- the tokens (string) on the images
- the 2D coordinates of each tokens
The tokens and their 2D position is provided by an OCR model. This model was trained using OCR results coming from Google Cloud Vision.
## Intended uses & limitations
This model is only intended to be used on images of products where a nutrition table can be found.
## Training and evaluation data
The training and evaluation data can be found on the [dataset page](https://huggingface.co/datasets/openfoodfacts/nutrient-detection-layout).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.9852 | 0.1664 | 15 | 1.1500 | 0.0 | 0.0 | 0.0 | 0.8101 |
| 1.0244 | 0.3329 | 30 | 0.8342 | 0.05 | 0.0074 | 0.0129 | 0.8123 |
| 0.7826 | 0.4993 | 45 | 0.6795 | 0.0789 | 0.1138 | 0.0932 | 0.8479 |
| 0.6767 | 0.6657 | 60 | 0.5963 | 0.1193 | 0.1644 | 0.1383 | 0.8578 |
| 0.6031 | 0.8322 | 75 | 0.5406 | 0.1671 | 0.2248 | 0.1917 | 0.8691 |
| 0.5756 | 0.9986 | 90 | 0.4935 | 0.2291 | 0.3112 | 0.2639 | 0.8778 |
| 0.5215 | 1.1650 | 105 | 0.4302 | 0.3267 | 0.3948 | 0.3575 | 0.8905 |
| 0.4782 | 1.3315 | 120 | 0.3782 | 0.3939 | 0.4654 | 0.4267 | 0.9020 |
| 0.4208 | 1.4979 | 135 | 0.3405 | 0.4027 | 0.5044 | 0.4478 | 0.9081 |
| 0.3532 | 1.6644 | 150 | 0.2930 | 0.4960 | 0.5820 | 0.5356 | 0.9252 |
| 0.3458 | 1.8308 | 165 | 0.2658 | 0.5155 | 0.6033 | 0.5560 | 0.9301 |
| 0.302 | 1.9972 | 180 | 0.2321 | 0.6112 | 0.7009 | 0.6530 | 0.9474 |
| 0.2655 | 2.1637 | 195 | 0.2093 | 0.6471 | 0.7264 | 0.6845 | 0.9520 |
| 0.2598 | 2.3301 | 210 | 0.1951 | 0.7013 | 0.7557 | 0.7275 | 0.9570 |
| 0.2364 | 2.4965 | 225 | 0.1794 | 0.7091 | 0.7743 | 0.7402 | 0.9590 |
| 0.2218 | 2.6630 | 240 | 0.1676 | 0.7216 | 0.7933 | 0.7558 | 0.9621 |
| 0.206 | 2.8294 | 255 | 0.1572 | 0.7436 | 0.8110 | 0.7758 | 0.9650 |
| 0.2053 | 2.9958 | 270 | 0.1580 | 0.7381 | 0.8114 | 0.7730 | 0.9640 |
| 0.1876 | 3.1623 | 285 | 0.1406 | 0.7738 | 0.8309 | 0.8013 | 0.9687 |
| 0.1602 | 3.3287 | 300 | 0.1420 | 0.7714 | 0.8277 | 0.7986 | 0.9671 |
| 0.1706 | 3.4951 | 315 | 0.1323 | 0.7933 | 0.8379 | 0.8150 | 0.9691 |
| 0.1585 | 3.6616 | 330 | 0.1313 | 0.8060 | 0.8551 | 0.8298 | 0.9700 |
| 0.1574 | 3.8280 | 345 | 0.1267 | 0.8129 | 0.8639 | 0.8376 | 0.9717 |
| 0.15 | 3.9945 | 360 | 0.1157 | 0.8336 | 0.8746 | 0.8536 | 0.9754 |
| 0.1192 | 4.1609 | 375 | 0.1120 | 0.8348 | 0.8709 | 0.8525 | 0.9741 |
| 0.1313 | 4.3273 | 390 | 0.1130 | 0.8395 | 0.8792 | 0.8589 | 0.9745 |
| 0.1179 | 4.4938 | 405 | 0.1093 | 0.8370 | 0.8871 | 0.8613 | 0.9755 |
| 0.1327 | 4.6602 | 420 | 0.1102 | 0.8400 | 0.8853 | 0.8621 | 0.9746 |
| 0.1323 | 4.8266 | 435 | 0.0997 | 0.8611 | 0.8987 | 0.8795 | 0.9782 |
| 0.1254 | 4.9931 | 450 | 0.0949 | 0.8499 | 0.8969 | 0.8728 | 0.9775 |
| 0.0999 | 5.1595 | 465 | 0.0847 | 0.8658 | 0.8992 | 0.8822 | 0.9797 |
| 0.1017 | 5.3259 | 480 | 0.0803 | 0.8747 | 0.9108 | 0.8924 | 0.9810 |
| 0.091 | 5.4924 | 495 | 0.0796 | 0.8784 | 0.9057 | 0.8918 | 0.9806 |
| 0.0979 | 5.6588 | 510 | 0.0943 | 0.8607 | 0.8950 | 0.8775 | 0.9773 |
| 0.1024 | 5.8252 | 525 | 0.0804 | 0.8710 | 0.9062 | 0.8882 | 0.9805 |
| 0.0952 | 5.9917 | 540 | 0.0787 | 0.8845 | 0.9178 | 0.9008 | 0.9816 |
| 0.0742 | 6.1581 | 555 | 0.0776 | 0.8918 | 0.9150 | 0.9033 | 0.9823 |
| 0.0764 | 6.3245 | 570 | 0.0721 | 0.9028 | 0.9187 | 0.9107 | 0.9837 |
| 0.0813 | 6.4910 | 585 | 0.0664 | 0.9065 | 0.9229 | 0.9146 | 0.9844 |
| 0.0791 | 6.6574 | 600 | 0.0642 | 0.9026 | 0.9252 | 0.9138 | 0.9848 |
| 0.0792 | 6.8239 | 615 | 0.0673 | 0.8964 | 0.9248 | 0.9104 | 0.9841 |
| 0.078 | 6.9903 | 630 | 0.0693 | 0.8938 | 0.9224 | 0.9079 | 0.9833 |
| 0.0678 | 7.1567 | 645 | 0.0672 | 0.9082 | 0.9327 | 0.9203 | 0.9852 |
| 0.0685 | 7.3232 | 660 | 0.0655 | 0.8926 | 0.9224 | 0.9073 | 0.9840 |
| 0.0555 | 7.4896 | 675 | 0.0615 | 0.9156 | 0.9271 | 0.9213 | 0.9856 |
| 0.07 | 7.6560 | 690 | 0.0587 | 0.9173 | 0.9373 | 0.9272 | 0.9868 |
| 0.065 | 7.8225 | 705 | 0.0558 | 0.9205 | 0.9405 | 0.9304 | 0.9875 |
| 0.0599 | 7.9889 | 720 | 0.0579 | 0.9253 | 0.9433 | 0.9342 | 0.9878 |
| 0.0571 | 8.1553 | 735 | 0.0593 | 0.9148 | 0.9331 | 0.9239 | 0.9866 |
| 0.0563 | 8.3218 | 750 | 0.0605 | 0.9152 | 0.9322 | 0.9236 | 0.9863 |
| 0.0602 | 8.4882 | 765 | 0.0581 | 0.9252 | 0.9308 | 0.9280 | 0.9863 |
| 0.0582 | 8.6546 | 780 | 0.0581 | 0.9206 | 0.9373 | 0.9289 | 0.9872 |
| 0.0514 | 8.8211 | 795 | 0.0557 | 0.9245 | 0.9382 | 0.9313 | 0.9873 |
| 0.0467 | 8.9875 | 810 | 0.0520 | 0.9291 | 0.9498 | 0.9394 | 0.9883 |
| 0.0435 | 9.1540 | 825 | 0.0526 | 0.9229 | 0.9447 | 0.9337 | 0.9880 |
| 0.0531 | 9.3204 | 840 | 0.0502 | 0.9249 | 0.9443 | 0.9345 | 0.9884 |
| 0.0502 | 9.4868 | 855 | 0.0545 | 0.9171 | 0.9452 | 0.9309 | 0.9874 |
| 0.0377 | 9.6533 | 870 | 0.0618 | 0.9077 | 0.9368 | 0.9221 | 0.9851 |
| 0.0416 | 9.8197 | 885 | 0.0549 | 0.9267 | 0.9392 | 0.9329 | 0.9881 |
| 0.044 | 9.9861 | 900 | 0.0529 | 0.9366 | 0.9475 | 0.9420 | 0.9884 |
| 0.0383 | 10.1526 | 915 | 0.0490 | 0.9332 | 0.9475 | 0.9403 | 0.9889 |
| 0.0454 | 10.3190 | 930 | 0.0507 | 0.9264 | 0.9471 | 0.9366 | 0.9885 |
| 0.0416 | 10.4854 | 945 | 0.0467 | 0.9364 | 0.9498 | 0.9430 | 0.9891 |
| 0.0403 | 10.6519 | 960 | 0.0499 | 0.9314 | 0.9457 | 0.9385 | 0.9886 |
| 0.0354 | 10.8183 | 975 | 0.0523 | 0.9258 | 0.9452 | 0.9354 | 0.9883 |
| 0.0338 | 10.9847 | 990 | 0.0521 | 0.9214 | 0.9424 | 0.9318 | 0.9880 |
| 0.0347 | 11.1512 | 1005 | 0.0539 | 0.9235 | 0.9475 | 0.9354 | 0.9880 |
| 0.0364 | 11.3176 | 1020 | 0.0560 | 0.9194 | 0.9480 | 0.9335 | 0.9871 |
| 0.0363 | 11.4840 | 1035 | 0.0509 | 0.9286 | 0.9480 | 0.9382 | 0.9889 |
| 0.0308 | 11.6505 | 1050 | 0.0498 | 0.9389 | 0.9484 | 0.9436 | 0.9893 |
| 0.032 | 11.8169 | 1065 | 0.0491 | 0.9364 | 0.9443 | 0.9403 | 0.9891 |
| 0.0331 | 11.9834 | 1080 | 0.0455 | 0.9373 | 0.9443 | 0.9408 | 0.9892 |
| 0.0301 | 12.1498 | 1095 | 0.0486 | 0.9359 | 0.9489 | 0.9423 | 0.9892 |
| 0.0308 | 12.3162 | 1110 | 0.0513 | 0.9325 | 0.9503 | 0.9413 | 0.9891 |
| 0.0253 | 12.4827 | 1125 | 0.0510 | 0.9296 | 0.9503 | 0.9398 | 0.9892 |
| 0.0301 | 12.6491 | 1140 | 0.0533 | 0.9308 | 0.9489 | 0.9397 | 0.9886 |
| 0.0328 | 12.8155 | 1155 | 0.0549 | 0.9287 | 0.9443 | 0.9364 | 0.9885 |
| 0.0298 | 12.9820 | 1170 | 0.0504 | 0.9402 | 0.9498 | 0.9450 | 0.9895 |
| 0.0256 | 13.1484 | 1185 | 0.0515 | 0.9354 | 0.9419 | 0.9387 | 0.9888 |
| 0.0313 | 13.3148 | 1200 | 0.0483 | 0.9418 | 0.9545 | 0.9481 | 0.9905 |
| 0.022 | 13.4813 | 1215 | 0.0463 | 0.9361 | 0.9531 | 0.9445 | 0.9899 |
| 0.0245 | 13.6477 | 1230 | 0.0494 | 0.9368 | 0.9494 | 0.9430 | 0.9893 |
| 0.0251 | 13.8141 | 1245 | 0.0493 | 0.9404 | 0.9531 | 0.9467 | 0.9898 |
| 0.0259 | 13.9806 | 1260 | 0.0511 | 0.9386 | 0.9522 | 0.9454 | 0.9895 |
| 0.03 | 14.1470 | 1275 | 0.0535 | 0.9344 | 0.9457 | 0.9400 | 0.9889 |
| 0.0192 | 14.3135 | 1290 | 0.0491 | 0.9428 | 0.9494 | 0.9461 | 0.9899 |
| 0.0267 | 14.4799 | 1305 | 0.0490 | 0.9457 | 0.9545 | 0.9501 | 0.9901 |
| 0.0241 | 14.6463 | 1320 | 0.0506 | 0.9435 | 0.9540 | 0.9487 | 0.9899 |
| 0.0211 | 14.8128 | 1335 | 0.0510 | 0.9444 | 0.9540 | 0.9492 | 0.9903 |
| 0.0171 | 14.9792 | 1350 | 0.0499 | 0.9405 | 0.9545 | 0.9474 | 0.9898 |
| 0.0226 | 15.1456 | 1365 | 0.0511 | 0.9366 | 0.9540 | 0.9452 | 0.9894 |
| 0.024 | 15.3121 | 1380 | 0.0484 | 0.9445 | 0.9559 | 0.9501 | 0.9899 |
| 0.018 | 15.4785 | 1395 | 0.0482 | 0.9469 | 0.9517 | 0.9493 | 0.9903 |
| 0.0191 | 15.6449 | 1410 | 0.0491 | 0.9442 | 0.9512 | 0.9477 | 0.9899 |
| 0.0203 | 15.8114 | 1425 | 0.0451 | 0.9510 | 0.9554 | 0.9532 | 0.9912 |
| 0.0198 | 15.9778 | 1440 | 0.0447 | 0.9497 | 0.9549 | 0.9523 | 0.9911 |
| 0.0167 | 16.1442 | 1455 | 0.0444 | 0.9487 | 0.9540 | 0.9514 | 0.9909 |
| 0.0178 | 16.3107 | 1470 | 0.0513 | 0.9386 | 0.9512 | 0.9449 | 0.9892 |
| 0.024 | 16.4771 | 1485 | 0.0502 | 0.9430 | 0.9536 | 0.9483 | 0.9899 |
| 0.0206 | 16.6436 | 1500 | 0.0459 | 0.9483 | 0.9545 | 0.9514 | 0.9908 |
| 0.0188 | 16.8100 | 1515 | 0.0469 | 0.9474 | 0.9540 | 0.9507 | 0.9906 |
| 0.016 | 16.9764 | 1530 | 0.0463 | 0.9468 | 0.9582 | 0.9524 | 0.9906 |
| 0.0161 | 17.1429 | 1545 | 0.0455 | 0.9516 | 0.9596 | 0.9556 | 0.9911 |
| 0.0135 | 17.3093 | 1560 | 0.0475 | 0.9524 | 0.9573 | 0.9548 | 0.9909 |
| 0.0148 | 17.4757 | 1575 | 0.0479 | 0.9440 | 0.9545 | 0.9492 | 0.9905 |
| 0.0173 | 17.6422 | 1590 | 0.0455 | 0.9539 | 0.9605 | 0.9572 | 0.9915 |
| 0.0173 | 17.8086 | 1605 | 0.0456 | 0.9475 | 0.9554 | 0.9514 | 0.9913 |
| 0.0185 | 17.9750 | 1620 | 0.0461 | 0.9498 | 0.9577 | 0.9537 | 0.9908 |
| 0.0153 | 18.1415 | 1635 | 0.0472 | 0.9491 | 0.9605 | 0.9548 | 0.9911 |
| 0.0148 | 18.3079 | 1650 | 0.0446 | 0.9507 | 0.9587 | 0.9547 | 0.9913 |
| 0.0136 | 18.4743 | 1665 | 0.0441 | 0.9486 | 0.9601 | 0.9543 | 0.9914 |
| 0.0185 | 18.6408 | 1680 | 0.0478 | 0.9528 | 0.9573 | 0.9551 | 0.9915 |
| 0.0147 | 18.8072 | 1695 | 0.0493 | 0.9515 | 0.9652 | 0.9583 | 0.9912 |
| 0.0156 | 18.9736 | 1710 | 0.0509 | 0.9440 | 0.9545 | 0.9492 | 0.9903 |
| 0.0113 | 19.1401 | 1725 | 0.0460 | 0.9559 | 0.9573 | 0.9566 | 0.9911 |
| 0.014 | 19.3065 | 1740 | 0.0493 | 0.9439 | 0.9526 | 0.9482 | 0.9905 |
| 0.0147 | 19.4730 | 1755 | 0.0498 | 0.9476 | 0.9568 | 0.9522 | 0.9906 |
| 0.0126 | 19.6394 | 1770 | 0.0493 | 0.9474 | 0.9531 | 0.9502 | 0.9906 |
| 0.0167 | 19.8058 | 1785 | 0.0491 | 0.9463 | 0.9577 | 0.9520 | 0.9904 |
| 0.0126 | 19.9723 | 1800 | 0.0474 | 0.9492 | 0.9540 | 0.9516 | 0.9908 |
| 0.0107 | 20.1387 | 1815 | 0.0462 | 0.9524 | 0.9577 | 0.9551 | 0.9914 |
| 0.0115 | 20.3051 | 1830 | 0.0481 | 0.9504 | 0.9614 | 0.9559 | 0.9911 |
| 0.0128 | 20.4716 | 1845 | 0.0486 | 0.9475 | 0.9563 | 0.9519 | 0.9907 |
| 0.0113 | 20.6380 | 1860 | 0.0491 | 0.9477 | 0.9591 | 0.9534 | 0.9910 |
| 0.0119 | 20.8044 | 1875 | 0.0514 | 0.9494 | 0.9503 | 0.9499 | 0.9901 |
| 0.0122 | 20.9709 | 1890 | 0.0480 | 0.9481 | 0.9591 | 0.9536 | 0.9911 |
| 0.0123 | 21.1373 | 1905 | 0.0477 | 0.9467 | 0.9577 | 0.9522 | 0.9909 |
| 0.0116 | 21.3037 | 1920 | 0.0486 | 0.9485 | 0.9582 | 0.9533 | 0.9910 |
| 0.0108 | 21.4702 | 1935 | 0.0488 | 0.9442 | 0.9582 | 0.9511 | 0.9905 |
| 0.0115 | 21.6366 | 1950 | 0.0472 | 0.9498 | 0.9587 | 0.9542 | 0.9913 |
| 0.0083 | 21.8031 | 1965 | 0.0476 | 0.9490 | 0.9596 | 0.9543 | 0.9911 |
| 0.0094 | 21.9695 | 1980 | 0.0475 | 0.9482 | 0.9605 | 0.9543 | 0.9909 |
| 0.0118 | 22.1359 | 1995 | 0.0492 | 0.9449 | 0.9554 | 0.9501 | 0.9904 |
| 0.01 | 22.3024 | 2010 | 0.0486 | 0.9492 | 0.9554 | 0.9523 | 0.9909 |
| 0.0114 | 22.4688 | 2025 | 0.0497 | 0.9502 | 0.9577 | 0.9540 | 0.9910 |
| 0.0091 | 22.6352 | 2040 | 0.0499 | 0.9503 | 0.9582 | 0.9542 | 0.9910 |
| 0.0077 | 22.8017 | 2055 | 0.0502 | 0.9513 | 0.9614 | 0.9563 | 0.9911 |
| 0.01 | 22.9681 | 2070 | 0.0513 | 0.9544 | 0.9628 | 0.9586 | 0.9913 |
| 0.0087 | 23.1345 | 2085 | 0.0485 | 0.9500 | 0.9610 | 0.9554 | 0.9912 |
| 0.0073 | 23.3010 | 2100 | 0.0485 | 0.9557 | 0.9628 | 0.9593 | 0.9917 |
| 0.0083 | 23.4674 | 2115 | 0.0485 | 0.9535 | 0.9610 | 0.9572 | 0.9913 |
| 0.0117 | 23.6338 | 2130 | 0.0479 | 0.9557 | 0.9624 | 0.9590 | 0.9916 |
| 0.0095 | 23.8003 | 2145 | 0.0508 | 0.9498 | 0.9587 | 0.9542 | 0.9911 |
| 0.009 | 23.9667 | 2160 | 0.0513 | 0.9492 | 0.9628 | 0.9560 | 0.9910 |
| 0.0077 | 24.1331 | 2175 | 0.0504 | 0.9553 | 0.9628 | 0.9591 | 0.9915 |
| 0.0087 | 24.2996 | 2190 | 0.0500 | 0.9521 | 0.9610 | 0.9565 | 0.9913 |
| 0.0068 | 24.4660 | 2205 | 0.0506 | 0.9539 | 0.9610 | 0.9574 | 0.9913 |
| 0.0094 | 24.6325 | 2220 | 0.0500 | 0.9507 | 0.9591 | 0.9549 | 0.9913 |
| 0.0088 | 24.7989 | 2235 | 0.0486 | 0.9508 | 0.9596 | 0.9552 | 0.9914 |
| 0.0089 | 24.9653 | 2250 | 0.0507 | 0.9508 | 0.9610 | 0.9559 | 0.9911 |
| 0.0063 | 25.1318 | 2265 | 0.0479 | 0.9561 | 0.9610 | 0.9585 | 0.9917 |
| 0.0058 | 25.2982 | 2280 | 0.0506 | 0.9526 | 0.9619 | 0.9572 | 0.9911 |
| 0.0102 | 25.4646 | 2295 | 0.0499 | 0.9526 | 0.9624 | 0.9575 | 0.9912 |
| 0.0079 | 25.6311 | 2310 | 0.0543 | 0.9469 | 0.9614 | 0.9541 | 0.9905 |
| 0.009 | 25.7975 | 2325 | 0.0498 | 0.9526 | 0.9619 | 0.9572 | 0.9915 |
| 0.0068 | 25.9639 | 2340 | 0.0511 | 0.9509 | 0.9619 | 0.9564 | 0.9911 |
| 0.007 | 26.1304 | 2355 | 0.0492 | 0.9527 | 0.9633 | 0.9580 | 0.9914 |
| 0.0086 | 26.2968 | 2370 | 0.0516 | 0.9500 | 0.9610 | 0.9554 | 0.9913 |
| 0.0078 | 26.4632 | 2385 | 0.0503 | 0.9504 | 0.9610 | 0.9557 | 0.9914 |
| 0.0067 | 26.6297 | 2400 | 0.0514 | 0.9527 | 0.9628 | 0.9577 | 0.9915 |
| 0.0059 | 26.7961 | 2415 | 0.0504 | 0.9549 | 0.9628 | 0.9588 | 0.9919 |
| 0.0089 | 26.9626 | 2430 | 0.0520 | 0.9517 | 0.9605 | 0.9561 | 0.9916 |
| 0.0059 | 27.1290 | 2445 | 0.0512 | 0.9522 | 0.9624 | 0.9573 | 0.9917 |
| 0.0073 | 27.2954 | 2460 | 0.0526 | 0.9530 | 0.9610 | 0.9570 | 0.9916 |
| 0.0065 | 27.4619 | 2475 | 0.0530 | 0.9527 | 0.9628 | 0.9577 | 0.9916 |
| 0.0064 | 27.6283 | 2490 | 0.0515 | 0.9535 | 0.9610 | 0.9572 | 0.9917 |
| 0.0072 | 27.7947 | 2505 | 0.0542 | 0.9482 | 0.9610 | 0.9546 | 0.9907 |
| 0.0066 | 27.9612 | 2520 | 0.0537 | 0.9491 | 0.9610 | 0.9550 | 0.9909 |
| 0.006 | 28.1276 | 2535 | 0.0518 | 0.9531 | 0.9628 | 0.9579 | 0.9915 |
| 0.0074 | 28.2940 | 2550 | 0.0523 | 0.9521 | 0.9610 | 0.9565 | 0.9914 |
| 0.0068 | 28.4605 | 2565 | 0.0534 | 0.9495 | 0.9614 | 0.9555 | 0.9913 |
| 0.0055 | 28.6269 | 2580 | 0.0521 | 0.9548 | 0.9619 | 0.9584 | 0.9917 |
| 0.0056 | 28.7933 | 2595 | 0.0526 | 0.9522 | 0.9614 | 0.9568 | 0.9913 |
| 0.0066 | 28.9598 | 2610 | 0.0527 | 0.9522 | 0.9619 | 0.9570 | 0.9913 |
| 0.0053 | 29.1262 | 2625 | 0.0533 | 0.9531 | 0.9628 | 0.9579 | 0.9913 |
| 0.0063 | 29.2926 | 2640 | 0.0520 | 0.9530 | 0.9610 | 0.9570 | 0.9913 |
| 0.0059 | 29.4591 | 2655 | 0.0533 | 0.9504 | 0.9605 | 0.9554 | 0.9910 |
| 0.0059 | 29.6255 | 2670 | 0.0532 | 0.9526 | 0.9619 | 0.9572 | 0.9912 |
| 0.0062 | 29.7920 | 2685 | 0.0516 | 0.9535 | 0.9624 | 0.9579 | 0.9917 |
| 0.0064 | 29.9584 | 2700 | 0.0515 | 0.9522 | 0.9624 | 0.9573 | 0.9915 |
| 0.0055 | 30.1248 | 2715 | 0.0513 | 0.9549 | 0.9633 | 0.9591 | 0.9917 |
| 0.0064 | 30.2913 | 2730 | 0.0524 | 0.9540 | 0.9628 | 0.9584 | 0.9916 |
| 0.0055 | 30.4577 | 2745 | 0.0530 | 0.9531 | 0.9633 | 0.9582 | 0.9915 |
| 0.0065 | 30.6241 | 2760 | 0.0528 | 0.9536 | 0.9642 | 0.9589 | 0.9917 |
| 0.0068 | 30.7906 | 2775 | 0.0530 | 0.9518 | 0.9633 | 0.9575 | 0.9916 |
| 0.0047 | 30.9570 | 2790 | 0.0545 | 0.9532 | 0.9647 | 0.9589 | 0.9916 |
| 0.0051 | 31.1234 | 2805 | 0.0534 | 0.9545 | 0.9647 | 0.9596 | 0.9917 |
| 0.0044 | 31.2899 | 2820 | 0.0532 | 0.9531 | 0.9633 | 0.9582 | 0.9914 |
| 0.0068 | 31.4563 | 2835 | 0.0532 | 0.9527 | 0.9633 | 0.9580 | 0.9913 |
| 0.0045 | 31.6227 | 2850 | 0.0531 | 0.9545 | 0.9638 | 0.9591 | 0.9915 |
| 0.0047 | 31.7892 | 2865 | 0.0530 | 0.9540 | 0.9633 | 0.9586 | 0.9916 |
| 0.0075 | 31.9556 | 2880 | 0.0533 | 0.9549 | 0.9638 | 0.9593 | 0.9916 |
| 0.0055 | 32.1221 | 2895 | 0.0525 | 0.9553 | 0.9638 | 0.9595 | 0.9917 |
| 0.006 | 32.2885 | 2910 | 0.0523 | 0.9553 | 0.9638 | 0.9595 | 0.9917 |
| 0.0062 | 32.4549 | 2925 | 0.0525 | 0.9544 | 0.9633 | 0.9589 | 0.9917 |
| 0.0059 | 32.6214 | 2940 | 0.0525 | 0.9549 | 0.9638 | 0.9593 | 0.9917 |
| 0.0058 | 32.7878 | 2955 | 0.0531 | 0.9549 | 0.9642 | 0.9596 | 0.9917 |
| 0.005 | 32.9542 | 2970 | 0.0533 | 0.9536 | 0.9633 | 0.9584 | 0.9916 |
| 0.007 | 33.1207 | 2985 | 0.0533 | 0.9536 | 0.9633 | 0.9584 | 0.9916 |
| 0.0047 | 33.2871 | 3000 | 0.0532 | 0.9536 | 0.9633 | 0.9584 | 0.9916 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.5.1
- Datasets 2.19.0
- Tokenizers 0.19.1
|