NLPmonster
commited on
layoutlmv3-for-complete-receipt-understanding
Browse files
README.md
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This model is a fine-tuned version of [NLPmonster/layoutlmv3-for-receipt-understanding](https://huggingface.co/NLPmonster/layoutlmv3-for-receipt-understanding) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Precision: 0.
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- Recall: 0.
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- F1: 0.
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- Accuracy: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.4.1+cu121
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- Tokenizers 0.19.1
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This model is a fine-tuned version of [NLPmonster/layoutlmv3-for-receipt-understanding](https://huggingface.co/NLPmonster/layoutlmv3-for-receipt-understanding) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4459
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- Precision: 0.8739
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- Recall: 0.8808
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- F1: 0.8773
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- Accuracy: 0.8751
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.923 | 0.4425 | 50 | 0.6403 | 0.8015 | 0.8231 | 0.8121 | 0.8043 |
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| 0.6865 | 0.8850 | 100 | 0.5970 | 0.8097 | 0.8356 | 0.8224 | 0.8132 |
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| 0.5204 | 1.3274 | 150 | 0.5144 | 0.8409 | 0.8406 | 0.8407 | 0.8375 |
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| 0.4726 | 1.7699 | 200 | 0.5400 | 0.8273 | 0.8420 | 0.8346 | 0.8292 |
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| 0.417 | 2.2124 | 250 | 0.5776 | 0.8107 | 0.8413 | 0.8257 | 0.8185 |
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| 0.3753 | 2.6549 | 300 | 0.4914 | 0.8358 | 0.8388 | 0.8373 | 0.8369 |
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| 0.3314 | 3.0973 | 350 | 0.5010 | 0.8265 | 0.8477 | 0.8370 | 0.8326 |
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| 0.2861 | 3.5398 | 400 | 0.4890 | 0.8486 | 0.8537 | 0.8512 | 0.8492 |
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| 0.2834 | 3.9823 | 450 | 0.5026 | 0.8396 | 0.8680 | 0.8535 | 0.8462 |
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| 0.2226 | 4.4248 | 500 | 0.4843 | 0.8476 | 0.8569 | 0.8522 | 0.8508 |
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| 0.2192 | 4.8673 | 550 | 0.4660 | 0.8527 | 0.8676 | 0.8601 | 0.856 |
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| 0.183 | 5.3097 | 600 | 0.4524 | 0.8592 | 0.8730 | 0.8660 | 0.8634 |
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| 0.1718 | 5.7522 | 650 | 0.4594 | 0.8579 | 0.8747 | 0.8663 | 0.8606 |
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| 0.1612 | 6.1947 | 700 | 0.4486 | 0.8663 | 0.8737 | 0.8700 | 0.8668 |
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| 0.136 | 6.6372 | 750 | 0.4589 | 0.8613 | 0.8662 | 0.8637 | 0.8594 |
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| 0.1261 | 7.0796 | 800 | 0.4282 | 0.8734 | 0.8740 | 0.8737 | 0.8708 |
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| 0.111 | 7.5221 | 850 | 0.4591 | 0.8692 | 0.8730 | 0.8711 | 0.8689 |
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| 0.1024 | 7.9646 | 900 | 0.4467 | 0.8720 | 0.8822 | 0.8771 | 0.8729 |
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| 0.0822 | 8.4071 | 950 | 0.4414 | 0.8711 | 0.8801 | 0.8756 | 0.8729 |
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| 0.0816 | 8.8496 | 1000 | 0.4459 | 0.8739 | 0.8808 | 0.8773 | 0.8751 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.4.1+cu121
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- Datasets 3.0.1
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- Tokenizers 0.19.1
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