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layoutlmv3
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End of training

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  1. README.md +21 -21
  2. model.safetensors +1 -1
  3. runs/Nov19_19-04-09_af1b30745784/events.out.tfevents.1732043052.af1b30745784.1683.0 +3 -0
  4. runs/Nov19_20-05-54_af1b30745784/events.out.tfevents.1732046858.af1b30745784.1683.1 +3 -0
  5. runs/Nov19_20-05-54_af1b30745784/events.out.tfevents.1732047624.af1b30745784.1683.2 +3 -0
  6. training_args.bin +1 -1
README.md CHANGED
@@ -26,16 +26,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.9583333333333334
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  - name: Recall
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  type: recall
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- value: 0.9663865546218487
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  - name: F1
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  type: f1
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- value: 0.9623430962343097
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  - name: Accuracy
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  type: accuracy
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- value: 0.9921787709497206
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -45,11 +45,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the layoutlmv3 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1046
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- - Precision: 0.9583
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- - Recall: 0.9664
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- - F1: 0.9623
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- - Accuracy: 0.9922
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  ## Model description
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@@ -78,18 +78,18 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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- |:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 1.3514 | 100 | 0.5502 | 0.7261 | 0.7017 | 0.7137 | 0.9006 |
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- | No log | 2.7027 | 200 | 0.1859 | 0.9034 | 0.9034 | 0.9034 | 0.9721 |
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- | No log | 4.0541 | 300 | 0.1437 | 0.9333 | 0.9412 | 0.9372 | 0.9844 |
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- | No log | 5.4054 | 400 | 0.1351 | 0.9256 | 0.9412 | 0.9333 | 0.9844 |
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- | 0.332 | 6.7568 | 500 | 0.1183 | 0.9380 | 0.9538 | 0.9458 | 0.9888 |
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- | 0.332 | 8.1081 | 600 | 0.1137 | 0.9502 | 0.9622 | 0.9562 | 0.9911 |
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- | 0.332 | 9.4595 | 700 | 0.1188 | 0.9502 | 0.9622 | 0.9562 | 0.9899 |
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- | 0.332 | 10.8108 | 800 | 0.1107 | 0.9583 | 0.9664 | 0.9623 | 0.9922 |
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- | 0.332 | 12.1622 | 900 | 0.1081 | 0.9583 | 0.9664 | 0.9623 | 0.9922 |
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- | 0.0141 | 13.5135 | 1000 | 0.1046 | 0.9583 | 0.9664 | 0.9623 | 0.9922 |
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  ### Framework versions
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9735537190082645
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  - name: Recall
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  type: recall
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+ value: 0.9751655629139073
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  - name: F1
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  type: f1
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+ value: 0.9743589743589743
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9924257137308216
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the layoutlmv3 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0899
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+ - Precision: 0.9736
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+ - Recall: 0.9752
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+ - F1: 0.9744
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+ - Accuracy: 0.9924
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 0.5291 | 100 | 0.4956 | 0.7821 | 0.7724 | 0.7772 | 0.9413 |
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+ | No log | 1.0582 | 200 | 0.1802 | 0.9285 | 0.9247 | 0.9266 | 0.9761 |
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+ | No log | 1.5873 | 300 | 0.1465 | 0.9334 | 0.9512 | 0.9422 | 0.9841 |
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+ | No log | 2.1164 | 400 | 0.1309 | 0.9447 | 0.9611 | 0.9528 | 0.9876 |
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+ | 0.3392 | 2.6455 | 500 | 0.1095 | 0.9516 | 0.9594 | 0.9555 | 0.9891 |
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+ | 0.3392 | 3.1746 | 600 | 0.1022 | 0.9573 | 0.9652 | 0.9613 | 0.9915 |
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+ | 0.3392 | 3.7037 | 700 | 0.1081 | 0.9573 | 0.9661 | 0.9617 | 0.9918 |
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+ | 0.3392 | 4.2328 | 800 | 0.0922 | 0.9726 | 0.9694 | 0.9710 | 0.9920 |
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+ | 0.3392 | 4.7619 | 900 | 0.0930 | 0.9702 | 0.9702 | 0.9702 | 0.9916 |
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+ | 0.0282 | 5.2910 | 1000 | 0.0899 | 0.9736 | 0.9752 | 0.9744 | 0.9924 |
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  ### Framework versions
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