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nutrition-extractor

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Duplicate from openfoodfacts/nutrition-extractor

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Co-authored-by: Raphaël <raphael0202@users.noreply.huggingface.co>

Files changed (26) hide show
  1. .gitattributes +35 -0
  2. README.md +301 -0
  3. all_results.json +18 -0
  4. config.json +187 -0
  5. eval_results.json +139 -0
  6. eval_results_agg.json +12 -0
  7. merges.txt +0 -0
  8. model.safetensors +3 -0
  9. onnx/config.json +186 -0
  10. onnx/merges.txt +0 -0
  11. onnx/model.onnx +3 -0
  12. onnx/preprocessor_config.json +26 -0
  13. onnx/special_tokens_map.json +51 -0
  14. onnx/tokenizer.json +0 -0
  15. onnx/tokenizer_config.json +87 -0
  16. onnx/vocab.json +0 -0
  17. preprocessor_config.json +43 -0
  18. runs/Dec23_15-55-22_gpu-l4-20241223-150914/events.out.tfevents.1734969342.gpu-l4-20241223-150914 +3 -0
  19. runs/Dec23_15-55-22_gpu-l4-20241223-150914/events.out.tfevents.1735024666.gpu-l4-20241223-150914 +3 -0
  20. special_tokens_map.json +51 -0
  21. tokenizer.json +0 -0
  22. tokenizer_config.json +80 -0
  23. train_results.json +9 -0
  24. trainer_state.json +0 -0
  25. training_args.bin +3 -0
  26. vocab.json +0 -0
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README.md ADDED
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+ ---
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+ license: cc-by-nc-sa-4.0
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+ base_model: microsoft/layoutlmv3-large
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - openfoodfacts/nutrient-detection-layout
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: nutrition-extractor
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+ results:
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+ - task:
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+ name: Token Classification
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+ type: token-classification
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+ dataset:
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+ name: openfoodfacts/nutrient-detection-layout
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+ type: openfoodfacts/nutrient-detection-layout
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.9545036764705882
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+ - name: Recall
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+ type: recall
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+ value: 0.9647004180213655
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+ - name: F1
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+ type: f1
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+ value: 0.9595749595749595
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9916725247390905
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+ ---
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+
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+
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+ # nutrition-extractor
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+
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+ This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on the openfoodfacts/nutrient-detection-layout dataset.
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+ It allows to automatically extract nutrition values from images of nutrition tables.
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+
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0534
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+ - Precision: 0.9545
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+ - Recall: 0.9647
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+ - F1: 0.9596
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+ - Accuracy: 0.9917
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+
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+ ## Model description
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+
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+ This model can extract nutrient values from photos of food packaging containing nutrition information. This was developped as part of the Nutrisight project.
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+
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+ 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.
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+
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+ As any model using the LayoutLM architecture, this model expects as input:
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+
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+ - the image
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+ - the tokens (string) on the images
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+ - the 2D coordinates of each token
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+
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+ The tokens and their 2D positions is provided by an OCR model. This model was trained using OCR results coming from Google Cloud Vision.
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+
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+ An ONNX export of the model can be found in the `onnx` directory.
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+
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+ ## Intended uses & limitations
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+
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+ This model is only intended to be used on images of products where a nutrition table can be found.
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+
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+ ## Training and evaluation data
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+
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+ The training and evaluation data can be found on the [dataset page](https://huggingface.co/datasets/openfoodfacts/nutrient-detection-layout).
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 1e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - gradient_accumulation_steps: 8
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+ - total_train_batch_size: 32
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - training_steps: 3000
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 1.9852 | 0.1664 | 15 | 1.1500 | 0.0 | 0.0 | 0.0 | 0.8101 |
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+ | 1.0244 | 0.3329 | 30 | 0.8342 | 0.05 | 0.0074 | 0.0129 | 0.8123 |
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+ | 0.7826 | 0.4993 | 45 | 0.6795 | 0.0789 | 0.1138 | 0.0932 | 0.8479 |
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+ | 0.6767 | 0.6657 | 60 | 0.5963 | 0.1193 | 0.1644 | 0.1383 | 0.8578 |
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+ | 0.6031 | 0.8322 | 75 | 0.5406 | 0.1671 | 0.2248 | 0.1917 | 0.8691 |
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+ | 0.5756 | 0.9986 | 90 | 0.4935 | 0.2291 | 0.3112 | 0.2639 | 0.8778 |
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+ | 0.5215 | 1.1650 | 105 | 0.4302 | 0.3267 | 0.3948 | 0.3575 | 0.8905 |
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+ | 0.4782 | 1.3315 | 120 | 0.3782 | 0.3939 | 0.4654 | 0.4267 | 0.9020 |
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+ | 0.4208 | 1.4979 | 135 | 0.3405 | 0.4027 | 0.5044 | 0.4478 | 0.9081 |
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+ | 0.3532 | 1.6644 | 150 | 0.2930 | 0.4960 | 0.5820 | 0.5356 | 0.9252 |
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+ | 0.3458 | 1.8308 | 165 | 0.2658 | 0.5155 | 0.6033 | 0.5560 | 0.9301 |
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+ | 0.302 | 1.9972 | 180 | 0.2321 | 0.6112 | 0.7009 | 0.6530 | 0.9474 |
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+ | 0.2655 | 2.1637 | 195 | 0.2093 | 0.6471 | 0.7264 | 0.6845 | 0.9520 |
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+ | 0.2598 | 2.3301 | 210 | 0.1951 | 0.7013 | 0.7557 | 0.7275 | 0.9570 |
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+ | 0.2364 | 2.4965 | 225 | 0.1794 | 0.7091 | 0.7743 | 0.7402 | 0.9590 |
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+ | 0.2218 | 2.6630 | 240 | 0.1676 | 0.7216 | 0.7933 | 0.7558 | 0.9621 |
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+ | 0.206 | 2.8294 | 255 | 0.1572 | 0.7436 | 0.8110 | 0.7758 | 0.9650 |
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+ | 0.2053 | 2.9958 | 270 | 0.1580 | 0.7381 | 0.8114 | 0.7730 | 0.9640 |
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+ | 0.1876 | 3.1623 | 285 | 0.1406 | 0.7738 | 0.8309 | 0.8013 | 0.9687 |
113
+ | 0.1602 | 3.3287 | 300 | 0.1420 | 0.7714 | 0.8277 | 0.7986 | 0.9671 |
114
+ | 0.1706 | 3.4951 | 315 | 0.1323 | 0.7933 | 0.8379 | 0.8150 | 0.9691 |
115
+ | 0.1585 | 3.6616 | 330 | 0.1313 | 0.8060 | 0.8551 | 0.8298 | 0.9700 |
116
+ | 0.1574 | 3.8280 | 345 | 0.1267 | 0.8129 | 0.8639 | 0.8376 | 0.9717 |
117
+ | 0.15 | 3.9945 | 360 | 0.1157 | 0.8336 | 0.8746 | 0.8536 | 0.9754 |
118
+ | 0.1192 | 4.1609 | 375 | 0.1120 | 0.8348 | 0.8709 | 0.8525 | 0.9741 |
119
+ | 0.1313 | 4.3273 | 390 | 0.1130 | 0.8395 | 0.8792 | 0.8589 | 0.9745 |
120
+ | 0.1179 | 4.4938 | 405 | 0.1093 | 0.8370 | 0.8871 | 0.8613 | 0.9755 |
121
+ | 0.1327 | 4.6602 | 420 | 0.1102 | 0.8400 | 0.8853 | 0.8621 | 0.9746 |
122
+ | 0.1323 | 4.8266 | 435 | 0.0997 | 0.8611 | 0.8987 | 0.8795 | 0.9782 |
123
+ | 0.1254 | 4.9931 | 450 | 0.0949 | 0.8499 | 0.8969 | 0.8728 | 0.9775 |
124
+ | 0.0999 | 5.1595 | 465 | 0.0847 | 0.8658 | 0.8992 | 0.8822 | 0.9797 |
125
+ | 0.1017 | 5.3259 | 480 | 0.0803 | 0.8747 | 0.9108 | 0.8924 | 0.9810 |
126
+ | 0.091 | 5.4924 | 495 | 0.0796 | 0.8784 | 0.9057 | 0.8918 | 0.9806 |
127
+ | 0.0979 | 5.6588 | 510 | 0.0943 | 0.8607 | 0.8950 | 0.8775 | 0.9773 |
128
+ | 0.1024 | 5.8252 | 525 | 0.0804 | 0.8710 | 0.9062 | 0.8882 | 0.9805 |
129
+ | 0.0952 | 5.9917 | 540 | 0.0787 | 0.8845 | 0.9178 | 0.9008 | 0.9816 |
130
+ | 0.0742 | 6.1581 | 555 | 0.0776 | 0.8918 | 0.9150 | 0.9033 | 0.9823 |
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+ | 0.0764 | 6.3245 | 570 | 0.0721 | 0.9028 | 0.9187 | 0.9107 | 0.9837 |
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+ | 0.0813 | 6.4910 | 585 | 0.0664 | 0.9065 | 0.9229 | 0.9146 | 0.9844 |
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+ | 0.0791 | 6.6574 | 600 | 0.0642 | 0.9026 | 0.9252 | 0.9138 | 0.9848 |
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+ | 0.0792 | 6.8239 | 615 | 0.0673 | 0.8964 | 0.9248 | 0.9104 | 0.9841 |
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+ | 0.078 | 6.9903 | 630 | 0.0693 | 0.8938 | 0.9224 | 0.9079 | 0.9833 |
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+ | 0.0678 | 7.1567 | 645 | 0.0672 | 0.9082 | 0.9327 | 0.9203 | 0.9852 |
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+ | 0.0685 | 7.3232 | 660 | 0.0655 | 0.8926 | 0.9224 | 0.9073 | 0.9840 |
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+ | 0.0555 | 7.4896 | 675 | 0.0615 | 0.9156 | 0.9271 | 0.9213 | 0.9856 |
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+ | 0.07 | 7.6560 | 690 | 0.0587 | 0.9173 | 0.9373 | 0.9272 | 0.9868 |
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+ | 0.065 | 7.8225 | 705 | 0.0558 | 0.9205 | 0.9405 | 0.9304 | 0.9875 |
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+ | 0.0599 | 7.9889 | 720 | 0.0579 | 0.9253 | 0.9433 | 0.9342 | 0.9878 |
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+ | 0.0571 | 8.1553 | 735 | 0.0593 | 0.9148 | 0.9331 | 0.9239 | 0.9866 |
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+ | 0.0563 | 8.3218 | 750 | 0.0605 | 0.9152 | 0.9322 | 0.9236 | 0.9863 |
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+ | 0.0602 | 8.4882 | 765 | 0.0581 | 0.9252 | 0.9308 | 0.9280 | 0.9863 |
145
+ | 0.0582 | 8.6546 | 780 | 0.0581 | 0.9206 | 0.9373 | 0.9289 | 0.9872 |
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+ | 0.0514 | 8.8211 | 795 | 0.0557 | 0.9245 | 0.9382 | 0.9313 | 0.9873 |
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+ | 0.0467 | 8.9875 | 810 | 0.0520 | 0.9291 | 0.9498 | 0.9394 | 0.9883 |
148
+ | 0.0435 | 9.1540 | 825 | 0.0526 | 0.9229 | 0.9447 | 0.9337 | 0.9880 |
149
+ | 0.0531 | 9.3204 | 840 | 0.0502 | 0.9249 | 0.9443 | 0.9345 | 0.9884 |
150
+ | 0.0502 | 9.4868 | 855 | 0.0545 | 0.9171 | 0.9452 | 0.9309 | 0.9874 |
151
+ | 0.0377 | 9.6533 | 870 | 0.0618 | 0.9077 | 0.9368 | 0.9221 | 0.9851 |
152
+ | 0.0416 | 9.8197 | 885 | 0.0549 | 0.9267 | 0.9392 | 0.9329 | 0.9881 |
153
+ | 0.044 | 9.9861 | 900 | 0.0529 | 0.9366 | 0.9475 | 0.9420 | 0.9884 |
154
+ | 0.0383 | 10.1526 | 915 | 0.0490 | 0.9332 | 0.9475 | 0.9403 | 0.9889 |
155
+ | 0.0454 | 10.3190 | 930 | 0.0507 | 0.9264 | 0.9471 | 0.9366 | 0.9885 |
156
+ | 0.0416 | 10.4854 | 945 | 0.0467 | 0.9364 | 0.9498 | 0.9430 | 0.9891 |
157
+ | 0.0403 | 10.6519 | 960 | 0.0499 | 0.9314 | 0.9457 | 0.9385 | 0.9886 |
158
+ | 0.0354 | 10.8183 | 975 | 0.0523 | 0.9258 | 0.9452 | 0.9354 | 0.9883 |
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+ | 0.0338 | 10.9847 | 990 | 0.0521 | 0.9214 | 0.9424 | 0.9318 | 0.9880 |
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+ | 0.0347 | 11.1512 | 1005 | 0.0539 | 0.9235 | 0.9475 | 0.9354 | 0.9880 |
161
+ | 0.0364 | 11.3176 | 1020 | 0.0560 | 0.9194 | 0.9480 | 0.9335 | 0.9871 |
162
+ | 0.0363 | 11.4840 | 1035 | 0.0509 | 0.9286 | 0.9480 | 0.9382 | 0.9889 |
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+ | 0.0308 | 11.6505 | 1050 | 0.0498 | 0.9389 | 0.9484 | 0.9436 | 0.9893 |
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+ | 0.032 | 11.8169 | 1065 | 0.0491 | 0.9364 | 0.9443 | 0.9403 | 0.9891 |
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+ | 0.0331 | 11.9834 | 1080 | 0.0455 | 0.9373 | 0.9443 | 0.9408 | 0.9892 |
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+ | 0.0301 | 12.1498 | 1095 | 0.0486 | 0.9359 | 0.9489 | 0.9423 | 0.9892 |
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+ | 0.0308 | 12.3162 | 1110 | 0.0513 | 0.9325 | 0.9503 | 0.9413 | 0.9891 |
168
+ | 0.0253 | 12.4827 | 1125 | 0.0510 | 0.9296 | 0.9503 | 0.9398 | 0.9892 |
169
+ | 0.0301 | 12.6491 | 1140 | 0.0533 | 0.9308 | 0.9489 | 0.9397 | 0.9886 |
170
+ | 0.0328 | 12.8155 | 1155 | 0.0549 | 0.9287 | 0.9443 | 0.9364 | 0.9885 |
171
+ | 0.0298 | 12.9820 | 1170 | 0.0504 | 0.9402 | 0.9498 | 0.9450 | 0.9895 |
172
+ | 0.0256 | 13.1484 | 1185 | 0.0515 | 0.9354 | 0.9419 | 0.9387 | 0.9888 |
173
+ | 0.0313 | 13.3148 | 1200 | 0.0483 | 0.9418 | 0.9545 | 0.9481 | 0.9905 |
174
+ | 0.022 | 13.4813 | 1215 | 0.0463 | 0.9361 | 0.9531 | 0.9445 | 0.9899 |
175
+ | 0.0245 | 13.6477 | 1230 | 0.0494 | 0.9368 | 0.9494 | 0.9430 | 0.9893 |
176
+ | 0.0251 | 13.8141 | 1245 | 0.0493 | 0.9404 | 0.9531 | 0.9467 | 0.9898 |
177
+ | 0.0259 | 13.9806 | 1260 | 0.0511 | 0.9386 | 0.9522 | 0.9454 | 0.9895 |
178
+ | 0.03 | 14.1470 | 1275 | 0.0535 | 0.9344 | 0.9457 | 0.9400 | 0.9889 |
179
+ | 0.0192 | 14.3135 | 1290 | 0.0491 | 0.9428 | 0.9494 | 0.9461 | 0.9899 |
180
+ | 0.0267 | 14.4799 | 1305 | 0.0490 | 0.9457 | 0.9545 | 0.9501 | 0.9901 |
181
+ | 0.0241 | 14.6463 | 1320 | 0.0506 | 0.9435 | 0.9540 | 0.9487 | 0.9899 |
182
+ | 0.0211 | 14.8128 | 1335 | 0.0510 | 0.9444 | 0.9540 | 0.9492 | 0.9903 |
183
+ | 0.0171 | 14.9792 | 1350 | 0.0499 | 0.9405 | 0.9545 | 0.9474 | 0.9898 |
184
+ | 0.0226 | 15.1456 | 1365 | 0.0511 | 0.9366 | 0.9540 | 0.9452 | 0.9894 |
185
+ | 0.024 | 15.3121 | 1380 | 0.0484 | 0.9445 | 0.9559 | 0.9501 | 0.9899 |
186
+ | 0.018 | 15.4785 | 1395 | 0.0482 | 0.9469 | 0.9517 | 0.9493 | 0.9903 |
187
+ | 0.0191 | 15.6449 | 1410 | 0.0491 | 0.9442 | 0.9512 | 0.9477 | 0.9899 |
188
+ | 0.0203 | 15.8114 | 1425 | 0.0451 | 0.9510 | 0.9554 | 0.9532 | 0.9912 |
189
+ | 0.0198 | 15.9778 | 1440 | 0.0447 | 0.9497 | 0.9549 | 0.9523 | 0.9911 |
190
+ | 0.0167 | 16.1442 | 1455 | 0.0444 | 0.9487 | 0.9540 | 0.9514 | 0.9909 |
191
+ | 0.0178 | 16.3107 | 1470 | 0.0513 | 0.9386 | 0.9512 | 0.9449 | 0.9892 |
192
+ | 0.024 | 16.4771 | 1485 | 0.0502 | 0.9430 | 0.9536 | 0.9483 | 0.9899 |
193
+ | 0.0206 | 16.6436 | 1500 | 0.0459 | 0.9483 | 0.9545 | 0.9514 | 0.9908 |
194
+ | 0.0188 | 16.8100 | 1515 | 0.0469 | 0.9474 | 0.9540 | 0.9507 | 0.9906 |
195
+ | 0.016 | 16.9764 | 1530 | 0.0463 | 0.9468 | 0.9582 | 0.9524 | 0.9906 |
196
+ | 0.0161 | 17.1429 | 1545 | 0.0455 | 0.9516 | 0.9596 | 0.9556 | 0.9911 |
197
+ | 0.0135 | 17.3093 | 1560 | 0.0475 | 0.9524 | 0.9573 | 0.9548 | 0.9909 |
198
+ | 0.0148 | 17.4757 | 1575 | 0.0479 | 0.9440 | 0.9545 | 0.9492 | 0.9905 |
199
+ | 0.0173 | 17.6422 | 1590 | 0.0455 | 0.9539 | 0.9605 | 0.9572 | 0.9915 |
200
+ | 0.0173 | 17.8086 | 1605 | 0.0456 | 0.9475 | 0.9554 | 0.9514 | 0.9913 |
201
+ | 0.0185 | 17.9750 | 1620 | 0.0461 | 0.9498 | 0.9577 | 0.9537 | 0.9908 |
202
+ | 0.0153 | 18.1415 | 1635 | 0.0472 | 0.9491 | 0.9605 | 0.9548 | 0.9911 |
203
+ | 0.0148 | 18.3079 | 1650 | 0.0446 | 0.9507 | 0.9587 | 0.9547 | 0.9913 |
204
+ | 0.0136 | 18.4743 | 1665 | 0.0441 | 0.9486 | 0.9601 | 0.9543 | 0.9914 |
205
+ | 0.0185 | 18.6408 | 1680 | 0.0478 | 0.9528 | 0.9573 | 0.9551 | 0.9915 |
206
+ | 0.0147 | 18.8072 | 1695 | 0.0493 | 0.9515 | 0.9652 | 0.9583 | 0.9912 |
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+ | 0.0156 | 18.9736 | 1710 | 0.0509 | 0.9440 | 0.9545 | 0.9492 | 0.9903 |
208
+ | 0.0113 | 19.1401 | 1725 | 0.0460 | 0.9559 | 0.9573 | 0.9566 | 0.9911 |
209
+ | 0.014 | 19.3065 | 1740 | 0.0493 | 0.9439 | 0.9526 | 0.9482 | 0.9905 |
210
+ | 0.0147 | 19.4730 | 1755 | 0.0498 | 0.9476 | 0.9568 | 0.9522 | 0.9906 |
211
+ | 0.0126 | 19.6394 | 1770 | 0.0493 | 0.9474 | 0.9531 | 0.9502 | 0.9906 |
212
+ | 0.0167 | 19.8058 | 1785 | 0.0491 | 0.9463 | 0.9577 | 0.9520 | 0.9904 |
213
+ | 0.0126 | 19.9723 | 1800 | 0.0474 | 0.9492 | 0.9540 | 0.9516 | 0.9908 |
214
+ | 0.0107 | 20.1387 | 1815 | 0.0462 | 0.9524 | 0.9577 | 0.9551 | 0.9914 |
215
+ | 0.0115 | 20.3051 | 1830 | 0.0481 | 0.9504 | 0.9614 | 0.9559 | 0.9911 |
216
+ | 0.0128 | 20.4716 | 1845 | 0.0486 | 0.9475 | 0.9563 | 0.9519 | 0.9907 |
217
+ | 0.0113 | 20.6380 | 1860 | 0.0491 | 0.9477 | 0.9591 | 0.9534 | 0.9910 |
218
+ | 0.0119 | 20.8044 | 1875 | 0.0514 | 0.9494 | 0.9503 | 0.9499 | 0.9901 |
219
+ | 0.0122 | 20.9709 | 1890 | 0.0480 | 0.9481 | 0.9591 | 0.9536 | 0.9911 |
220
+ | 0.0123 | 21.1373 | 1905 | 0.0477 | 0.9467 | 0.9577 | 0.9522 | 0.9909 |
221
+ | 0.0116 | 21.3037 | 1920 | 0.0486 | 0.9485 | 0.9582 | 0.9533 | 0.9910 |
222
+ | 0.0108 | 21.4702 | 1935 | 0.0488 | 0.9442 | 0.9582 | 0.9511 | 0.9905 |
223
+ | 0.0115 | 21.6366 | 1950 | 0.0472 | 0.9498 | 0.9587 | 0.9542 | 0.9913 |
224
+ | 0.0083 | 21.8031 | 1965 | 0.0476 | 0.9490 | 0.9596 | 0.9543 | 0.9911 |
225
+ | 0.0094 | 21.9695 | 1980 | 0.0475 | 0.9482 | 0.9605 | 0.9543 | 0.9909 |
226
+ | 0.0118 | 22.1359 | 1995 | 0.0492 | 0.9449 | 0.9554 | 0.9501 | 0.9904 |
227
+ | 0.01 | 22.3024 | 2010 | 0.0486 | 0.9492 | 0.9554 | 0.9523 | 0.9909 |
228
+ | 0.0114 | 22.4688 | 2025 | 0.0497 | 0.9502 | 0.9577 | 0.9540 | 0.9910 |
229
+ | 0.0091 | 22.6352 | 2040 | 0.0499 | 0.9503 | 0.9582 | 0.9542 | 0.9910 |
230
+ | 0.0077 | 22.8017 | 2055 | 0.0502 | 0.9513 | 0.9614 | 0.9563 | 0.9911 |
231
+ | 0.01 | 22.9681 | 2070 | 0.0513 | 0.9544 | 0.9628 | 0.9586 | 0.9913 |
232
+ | 0.0087 | 23.1345 | 2085 | 0.0485 | 0.9500 | 0.9610 | 0.9554 | 0.9912 |
233
+ | 0.0073 | 23.3010 | 2100 | 0.0485 | 0.9557 | 0.9628 | 0.9593 | 0.9917 |
234
+ | 0.0083 | 23.4674 | 2115 | 0.0485 | 0.9535 | 0.9610 | 0.9572 | 0.9913 |
235
+ | 0.0117 | 23.6338 | 2130 | 0.0479 | 0.9557 | 0.9624 | 0.9590 | 0.9916 |
236
+ | 0.0095 | 23.8003 | 2145 | 0.0508 | 0.9498 | 0.9587 | 0.9542 | 0.9911 |
237
+ | 0.009 | 23.9667 | 2160 | 0.0513 | 0.9492 | 0.9628 | 0.9560 | 0.9910 |
238
+ | 0.0077 | 24.1331 | 2175 | 0.0504 | 0.9553 | 0.9628 | 0.9591 | 0.9915 |
239
+ | 0.0087 | 24.2996 | 2190 | 0.0500 | 0.9521 | 0.9610 | 0.9565 | 0.9913 |
240
+ | 0.0068 | 24.4660 | 2205 | 0.0506 | 0.9539 | 0.9610 | 0.9574 | 0.9913 |
241
+ | 0.0094 | 24.6325 | 2220 | 0.0500 | 0.9507 | 0.9591 | 0.9549 | 0.9913 |
242
+ | 0.0088 | 24.7989 | 2235 | 0.0486 | 0.9508 | 0.9596 | 0.9552 | 0.9914 |
243
+ | 0.0089 | 24.9653 | 2250 | 0.0507 | 0.9508 | 0.9610 | 0.9559 | 0.9911 |
244
+ | 0.0063 | 25.1318 | 2265 | 0.0479 | 0.9561 | 0.9610 | 0.9585 | 0.9917 |
245
+ | 0.0058 | 25.2982 | 2280 | 0.0506 | 0.9526 | 0.9619 | 0.9572 | 0.9911 |
246
+ | 0.0102 | 25.4646 | 2295 | 0.0499 | 0.9526 | 0.9624 | 0.9575 | 0.9912 |
247
+ | 0.0079 | 25.6311 | 2310 | 0.0543 | 0.9469 | 0.9614 | 0.9541 | 0.9905 |
248
+ | 0.009 | 25.7975 | 2325 | 0.0498 | 0.9526 | 0.9619 | 0.9572 | 0.9915 |
249
+ | 0.0068 | 25.9639 | 2340 | 0.0511 | 0.9509 | 0.9619 | 0.9564 | 0.9911 |
250
+ | 0.007 | 26.1304 | 2355 | 0.0492 | 0.9527 | 0.9633 | 0.9580 | 0.9914 |
251
+ | 0.0086 | 26.2968 | 2370 | 0.0516 | 0.9500 | 0.9610 | 0.9554 | 0.9913 |
252
+ | 0.0078 | 26.4632 | 2385 | 0.0503 | 0.9504 | 0.9610 | 0.9557 | 0.9914 |
253
+ | 0.0067 | 26.6297 | 2400 | 0.0514 | 0.9527 | 0.9628 | 0.9577 | 0.9915 |
254
+ | 0.0059 | 26.7961 | 2415 | 0.0504 | 0.9549 | 0.9628 | 0.9588 | 0.9919 |
255
+ | 0.0089 | 26.9626 | 2430 | 0.0520 | 0.9517 | 0.9605 | 0.9561 | 0.9916 |
256
+ | 0.0059 | 27.1290 | 2445 | 0.0512 | 0.9522 | 0.9624 | 0.9573 | 0.9917 |
257
+ | 0.0073 | 27.2954 | 2460 | 0.0526 | 0.9530 | 0.9610 | 0.9570 | 0.9916 |
258
+ | 0.0065 | 27.4619 | 2475 | 0.0530 | 0.9527 | 0.9628 | 0.9577 | 0.9916 |
259
+ | 0.0064 | 27.6283 | 2490 | 0.0515 | 0.9535 | 0.9610 | 0.9572 | 0.9917 |
260
+ | 0.0072 | 27.7947 | 2505 | 0.0542 | 0.9482 | 0.9610 | 0.9546 | 0.9907 |
261
+ | 0.0066 | 27.9612 | 2520 | 0.0537 | 0.9491 | 0.9610 | 0.9550 | 0.9909 |
262
+ | 0.006 | 28.1276 | 2535 | 0.0518 | 0.9531 | 0.9628 | 0.9579 | 0.9915 |
263
+ | 0.0074 | 28.2940 | 2550 | 0.0523 | 0.9521 | 0.9610 | 0.9565 | 0.9914 |
264
+ | 0.0068 | 28.4605 | 2565 | 0.0534 | 0.9495 | 0.9614 | 0.9555 | 0.9913 |
265
+ | 0.0055 | 28.6269 | 2580 | 0.0521 | 0.9548 | 0.9619 | 0.9584 | 0.9917 |
266
+ | 0.0056 | 28.7933 | 2595 | 0.0526 | 0.9522 | 0.9614 | 0.9568 | 0.9913 |
267
+ | 0.0066 | 28.9598 | 2610 | 0.0527 | 0.9522 | 0.9619 | 0.9570 | 0.9913 |
268
+ | 0.0053 | 29.1262 | 2625 | 0.0533 | 0.9531 | 0.9628 | 0.9579 | 0.9913 |
269
+ | 0.0063 | 29.2926 | 2640 | 0.0520 | 0.9530 | 0.9610 | 0.9570 | 0.9913 |
270
+ | 0.0059 | 29.4591 | 2655 | 0.0533 | 0.9504 | 0.9605 | 0.9554 | 0.9910 |
271
+ | 0.0059 | 29.6255 | 2670 | 0.0532 | 0.9526 | 0.9619 | 0.9572 | 0.9912 |
272
+ | 0.0062 | 29.7920 | 2685 | 0.0516 | 0.9535 | 0.9624 | 0.9579 | 0.9917 |
273
+ | 0.0064 | 29.9584 | 2700 | 0.0515 | 0.9522 | 0.9624 | 0.9573 | 0.9915 |
274
+ | 0.0055 | 30.1248 | 2715 | 0.0513 | 0.9549 | 0.9633 | 0.9591 | 0.9917 |
275
+ | 0.0064 | 30.2913 | 2730 | 0.0524 | 0.9540 | 0.9628 | 0.9584 | 0.9916 |
276
+ | 0.0055 | 30.4577 | 2745 | 0.0530 | 0.9531 | 0.9633 | 0.9582 | 0.9915 |
277
+ | 0.0065 | 30.6241 | 2760 | 0.0528 | 0.9536 | 0.9642 | 0.9589 | 0.9917 |
278
+ | 0.0068 | 30.7906 | 2775 | 0.0530 | 0.9518 | 0.9633 | 0.9575 | 0.9916 |
279
+ | 0.0047 | 30.9570 | 2790 | 0.0545 | 0.9532 | 0.9647 | 0.9589 | 0.9916 |
280
+ | 0.0051 | 31.1234 | 2805 | 0.0534 | 0.9545 | 0.9647 | 0.9596 | 0.9917 |
281
+ | 0.0044 | 31.2899 | 2820 | 0.0532 | 0.9531 | 0.9633 | 0.9582 | 0.9914 |
282
+ | 0.0068 | 31.4563 | 2835 | 0.0532 | 0.9527 | 0.9633 | 0.9580 | 0.9913 |
283
+ | 0.0045 | 31.6227 | 2850 | 0.0531 | 0.9545 | 0.9638 | 0.9591 | 0.9915 |
284
+ | 0.0047 | 31.7892 | 2865 | 0.0530 | 0.9540 | 0.9633 | 0.9586 | 0.9916 |
285
+ | 0.0075 | 31.9556 | 2880 | 0.0533 | 0.9549 | 0.9638 | 0.9593 | 0.9916 |
286
+ | 0.0055 | 32.1221 | 2895 | 0.0525 | 0.9553 | 0.9638 | 0.9595 | 0.9917 |
287
+ | 0.006 | 32.2885 | 2910 | 0.0523 | 0.9553 | 0.9638 | 0.9595 | 0.9917 |
288
+ | 0.0062 | 32.4549 | 2925 | 0.0525 | 0.9544 | 0.9633 | 0.9589 | 0.9917 |
289
+ | 0.0059 | 32.6214 | 2940 | 0.0525 | 0.9549 | 0.9638 | 0.9593 | 0.9917 |
290
+ | 0.0058 | 32.7878 | 2955 | 0.0531 | 0.9549 | 0.9642 | 0.9596 | 0.9917 |
291
+ | 0.005 | 32.9542 | 2970 | 0.0533 | 0.9536 | 0.9633 | 0.9584 | 0.9916 |
292
+ | 0.007 | 33.1207 | 2985 | 0.0533 | 0.9536 | 0.9633 | 0.9584 | 0.9916 |
293
+ | 0.0047 | 33.2871 | 3000 | 0.0532 | 0.9536 | 0.9633 | 0.9584 | 0.9916 |
294
+
295
+
296
+ ### Framework versions
297
+
298
+ - Transformers 4.40.2
299
+ - Pytorch 2.5.1
300
+ - Datasets 2.19.0
301
+ - Tokenizers 0.19.1
all_results.json ADDED
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1
+ {
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+ "epoch": 33.287101248266296,
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+ "eval_precision": 0.9545036764705882,
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+ "eval_recall": 0.9647004180213655,
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+ "eval_samples": 199,
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+ "eval_samples_per_second": 5.576,
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+ "eval_steps_per_second": 1.401,
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+ "total_flos": 8.9780255686656e+16,
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+ "train_loss": 0.0764370101193587,
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+ "train_samples": 2884,
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+ "train_samples_per_second": 1.738,
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+ "train_steps_per_second": 0.054
18
+ }
config.json ADDED
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1
+ {
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+ "_name_or_path": "microsoft/layoutlmv3-large",
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+ "architectures": [
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+ "LayoutLMv3ForTokenClassification"
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+ ],
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+ "has_relative_attention_bias": true,
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+ "has_spatial_attention_bias": true,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 1024,
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+ "id2label": {
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+ "0": "O",
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+ "1": "B-ENERGY_KJ_100G",
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+ "2": "I-ENERGY_KJ_100G",
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+ "3": "B-VITAMIN_D_SERVING",
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+ "4": "I-VITAMIN_D_SERVING",
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+ "5": "B-SODIUM_SERVING",
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+ "6": "I-SODIUM_SERVING",
25
+ "7": "B-PROTEINS_SERVING",
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+ "8": "I-PROTEINS_SERVING",
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+ "9": "B-ADDED_SUGARS_SERVING",
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+ "10": "I-ADDED_SUGARS_SERVING",
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+ "11": "B-CALCIUM_SERVING",
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+ "12": "I-CALCIUM_SERVING",
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+ "13": "B-FAT_SERVING",
32
+ "14": "I-FAT_SERVING",
33
+ "15": "B-ENERGY_KJ_SERVING",
34
+ "16": "I-ENERGY_KJ_SERVING",
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+ "17": "B-SUGARS_100G",
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+ "18": "I-SUGARS_100G",
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+ "19": "B-SATURATED_FAT_SERVING",
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+ "20": "I-SATURATED_FAT_SERVING",
39
+ "21": "B-SERVING_SIZE",
40
+ "22": "I-SERVING_SIZE",
41
+ "23": "B-SALT_SERVING",
42
+ "24": "I-SALT_SERVING",
43
+ "25": "B-ENERGY_KCAL_SERVING",
44
+ "26": "I-ENERGY_KCAL_SERVING",
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+ "27": "B-FAT_100G",
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+ "28": "I-FAT_100G",
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+ "29": "B-SUGARS_SERVING",
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+ "30": "I-SUGARS_SERVING",
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+ "31": "B-FIBER_SERVING",
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+ "32": "I-FIBER_SERVING",
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+ "33": "B-TRANS_FAT_SERVING",
52
+ "34": "I-TRANS_FAT_SERVING",
53
+ "35": "B-POTASSIUM_SERVING",
54
+ "36": "I-POTASSIUM_SERVING",
55
+ "37": "B-CARBOHYDRATES_100G",
56
+ "38": "I-CARBOHYDRATES_100G",
57
+ "39": "B-POTASSIUM_100G",
58
+ "40": "I-POTASSIUM_100G",
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+ "41": "B-IRON_SERVING",
60
+ "42": "I-IRON_SERVING",
61
+ "43": "B-CHOLESTEROL_100G",
62
+ "44": "I-CHOLESTEROL_100G",
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+ "45": "B-TRANS_FAT_100G",
64
+ "46": "I-TRANS_FAT_100G",
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+ "47": "B-ADDED_SUGARS_100G",
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+ "48": "I-ADDED_SUGARS_100G",
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+ "49": "B-FIBER_100G",
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+ "50": "I-FIBER_100G",
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+ "51": "B-CALCIUM_100G",
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+ "52": "I-CALCIUM_100G",
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+ "53": "B-SODIUM_100G",
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+ "54": "I-SODIUM_100G",
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+ "55": "B-ENERGY_KCAL_100G",
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+ "56": "I-ENERGY_KCAL_100G",
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+ "57": "B-CHOLESTEROL_SERVING",
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+ "58": "I-CHOLESTEROL_SERVING",
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+ "59": "B-CARBOHYDRATES_SERVING",
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+ "60": "I-CARBOHYDRATES_SERVING",
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+ "61": "B-SALT_100G",
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+ "62": "I-SALT_100G",
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+ "63": "B-VITAMIN_D_100G",
82
+ "64": "I-VITAMIN_D_100G",
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+ "65": "B-SATURATED_FAT_100G",
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+ "66": "I-SATURATED_FAT_100G",
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+ "67": "B-PROTEINS_100G",
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+ "68": "I-PROTEINS_100G",
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+ "69": "B-IRON_100G",
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+ "70": "I-IRON_100G"
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+ },
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+ "initializer_range": 0.02,
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+ "input_size": 224,
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+ "intermediate_size": 4096,
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+ "label2id": {
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+ "B-ADDED_SUGARS_SERVING": 9,
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+ "B-CALCIUM_SERVING": 11,
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+ "B-CARBOHYDRATES_100G": 37,
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+ "B-CHOLESTEROL_SERVING": 57,
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+ "B-ENERGY_KCAL_100G": 55,
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+ "B-ENERGY_KCAL_SERVING": 25,
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+ "B-ENERGY_KJ_100G": 1,
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+ "B-FIBER_100G": 49,
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+ "B-FIBER_SERVING": 31,
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+ "B-IRON_100G": 69,
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+ "B-IRON_SERVING": 41,
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+ "B-POTASSIUM_100G": 39,
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+ "B-PROTEINS_100G": 67,
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+ "B-PROTEINS_SERVING": 7,
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+ "B-SALT_100G": 61,
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+ "B-SALT_SERVING": 23,
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+ "B-SATURATED_FAT_100G": 65,
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+ "B-SATURATED_FAT_SERVING": 19,
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+ "B-SERVING_SIZE": 21,
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+ "B-SUGARS_SERVING": 29,
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+ "B-TRANS_FAT_100G": 45,
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+ "B-TRANS_FAT_SERVING": 33,
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+ "B-VITAMIN_D_100G": 63,
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+ "B-VITAMIN_D_SERVING": 3,
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+ "I-ADDED_SUGARS_100G": 48,
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+ "I-ADDED_SUGARS_SERVING": 10,
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+ "I-CALCIUM_100G": 52,
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+ "I-CALCIUM_SERVING": 12,
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+ "I-CARBOHYDRATES_100G": 38,
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+ "I-CHOLESTEROL_100G": 44,
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+ "I-CHOLESTEROL_SERVING": 58,
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+ "I-PROTEINS_100G": 68,
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+ "I-PROTEINS_SERVING": 8,
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+ "I-SALT_100G": 62,
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+ "I-SATURATED_FAT_100G": 66,
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+ "I-SERVING_SIZE": 22,
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+ "I-SODIUM_100G": 54,
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+ "I-SODIUM_SERVING": 6,
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+ "I-SUGARS_100G": 18,
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+ "I-SUGARS_SERVING": 30,
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+ "I-TRANS_FAT_100G": 46,
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+ "I-TRANS_FAT_SERVING": 34,
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+ "I-VITAMIN_D_100G": 64,
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+ "I-VITAMIN_D_SERVING": 4,
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+ "O": 0
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+ },
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+ "layer_norm_eps": 1e-05,
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+ "max_2d_position_embeddings": 1024,
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+ "max_position_embeddings": 514,
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+ "max_rel_2d_pos": 256,
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+ "max_rel_pos": 128,
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+ "num_attention_heads": 16,
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+ "rel_pos_bins": 32,
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+ "text_embed": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.40.2",
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+ "type_vocab_size": 1,
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+ "visual_embed": true,
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+ "vocab_size": 50265
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+ }
eval_results.json ADDED
@@ -0,0 +1,139 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "eval_ADDED_SUGARS_SERVING_f1": 0.5714285714285715,
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+ "eval_ADDED_SUGARS_SERVING_number": 3,
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