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TomasFAV
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Layoutlmv3InvoiceCzechV012

Token Classification
Transformers
TensorBoard
Safetensors
layoutlmv3
Generated from Trainer
invoice-processing
information-extraction
czech-language
document-ai
layout-aware-model
multimodal-model
synthetic-data
hybrid-data
Model card Files
xet
 Metrics Community
TomasFAV commited on
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  1. README.md +15 -15
  2. model.safetensors +1 -1
  3. runs/Mar09_00-24-38_19d8f43ec3fa/events.out.tfevents.1773016610.19d8f43ec3fa.17345.1 +3 -0
README.md CHANGED
@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model was trained from scratch on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0614
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- - Precision: 0.8392
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- - Recall: 0.8770
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- - F1: 0.8577
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- - Accuracy: 0.9887
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  ## Model description
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@@ -56,16 +56,16 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 1.0 | 115 | 0.0663 | 0.7486 | 0.8115 | 0.7788 | 0.9841 |
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- | No log | 2.0 | 230 | 0.0618 | 0.7601 | 0.8443 | 0.8000 | 0.9854 |
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- | No log | 3.0 | 345 | 0.0726 | 0.7471 | 0.8053 | 0.7751 | 0.9837 |
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- | No log | 4.0 | 460 | 0.0650 | 0.7926 | 0.8381 | 0.8147 | 0.9856 |
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- | 0.0529 | 5.0 | 575 | 0.0682 | 0.7970 | 0.8607 | 0.8276 | 0.9870 |
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- | 0.0529 | 6.0 | 690 | 0.0718 | 0.7585 | 0.8689 | 0.8099 | 0.9845 |
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- | 0.0529 | 7.0 | 805 | 0.0614 | 0.8392 | 0.8770 | 0.8577 | 0.9887 |
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- | 0.0529 | 8.0 | 920 | 0.0679 | 0.7901 | 0.8873 | 0.8359 | 0.9867 |
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- | 0.0188 | 9.0 | 1035 | 0.0669 | 0.7978 | 0.8811 | 0.8374 | 0.9871 |
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- | 0.0188 | 10.0 | 1150 | 0.0667 | 0.8068 | 0.8811 | 0.8423 | 0.9874 |
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  ### Framework versions
 
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  This model was trained from scratch on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0763
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+ - Precision: 0.8009
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+ - Recall: 0.8849
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+ - F1: 0.8408
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+ - Accuracy: 0.9844
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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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+ | No log | 1.0 | 115 | 0.0725 | 0.7496 | 0.8257 | 0.7858 | 0.9807 |
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+ | No log | 2.0 | 230 | 0.0701 | 0.7569 | 0.8376 | 0.7952 | 0.9822 |
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+ | No log | 3.0 | 345 | 0.0735 | 0.7587 | 0.8883 | 0.8184 | 0.9810 |
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+ | No log | 4.0 | 460 | 0.0743 | 0.7827 | 0.8714 | 0.8247 | 0.9826 |
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+ | 0.0606 | 5.0 | 575 | 0.0783 | 0.7756 | 0.8714 | 0.8207 | 0.9821 |
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+ | 0.0606 | 6.0 | 690 | 0.0811 | 0.7561 | 0.8968 | 0.8204 | 0.9814 |
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+ | 0.0606 | 7.0 | 805 | 0.0763 | 0.8009 | 0.8849 | 0.8408 | 0.9844 |
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+ | 0.0606 | 8.0 | 920 | 0.0826 | 0.7784 | 0.9036 | 0.8363 | 0.9835 |
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+ | 0.0201 | 9.0 | 1035 | 0.0824 | 0.7837 | 0.8951 | 0.8357 | 0.9836 |
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+ | 0.0201 | 10.0 | 1150 | 0.0852 | 0.7818 | 0.9036 | 0.8383 | 0.9834 |
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  ### Framework versions
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