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Token Classification
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layoutlmv3
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End of training

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  1. README.md +20 -20
  2. model.safetensors +1 -1
  3. runs/Nov18_15-21-34_389b71462727/events.out.tfevents.1731943296.389b71462727.2719.0 +3 -0
  4. runs/Nov18_15-21-34_389b71462727/events.out.tfevents.1731943929.389b71462727.2719.1 +3 -0
  5. 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: 0.8860759493670886
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  - name: Recall
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  type: recall
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- value: 0.9210526315789473
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  - name: F1
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  type: f1
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- value: 0.9032258064516129
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  - name: Accuracy
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  type: accuracy
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- value: 0.94375
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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: 0.3028
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- - Precision: 0.8861
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- - Recall: 0.9211
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- - F1: 0.9032
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- - Accuracy: 0.9437
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  ## Model description
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@@ -80,21 +80,21 @@ 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 | 4.3478 | 100 | 0.6867 | 0.6842 | 0.6842 | 0.6842 | 0.8063 |
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- | No log | 8.6957 | 200 | 0.2381 | 0.8625 | 0.9079 | 0.8846 | 0.9313 |
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- | No log | 13.0435 | 300 | 0.2598 | 0.8846 | 0.9079 | 0.8961 | 0.9313 |
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- | No log | 17.3913 | 400 | 0.2165 | 0.8625 | 0.9079 | 0.8846 | 0.9375 |
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- | 0.3281 | 21.7391 | 500 | 0.2037 | 0.8625 | 0.9079 | 0.8846 | 0.9375 |
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- | 0.3281 | 26.0870 | 600 | 0.2571 | 0.8861 | 0.9211 | 0.9032 | 0.9437 |
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- | 0.3281 | 30.4348 | 700 | 0.2735 | 0.8861 | 0.9211 | 0.9032 | 0.9437 |
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- | 0.3281 | 34.7826 | 800 | 0.2993 | 0.8861 | 0.9211 | 0.9032 | 0.9437 |
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- | 0.3281 | 39.1304 | 900 | 0.3044 | 0.8861 | 0.9211 | 0.9032 | 0.9437 |
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- | 0.012 | 43.4783 | 1000 | 0.3028 | 0.8861 | 0.9211 | 0.9032 | 0.9437 |
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  ### Framework versions
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  - Transformers 4.47.0.dev0
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- - Pytorch 2.5.0+cu121
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  - Datasets 3.1.0
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  - Tokenizers 0.20.3
 
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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
 
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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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  | 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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  - Transformers 4.47.0.dev0
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+ - Pytorch 2.5.1+cu121
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  - Datasets 3.1.0
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  - Tokenizers 0.20.3
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