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layoutlmv3-base-finetuned-FUNSD

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layoutlmv3
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Vineetttt/layoutlmv3-base-finetuned-FUNSD

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  1. README.md +19 -19
  2. pytorch_model.bin +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.8892100192678227
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  - name: Recall
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  type: recall
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- value: 0.9170392449080974
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  - name: F1
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  type: f1
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- value: 0.9029102470041576
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  - name: Accuracy
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  type: accuracy
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- value: 0.8690122429573279
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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
@@ -44,11 +44,11 @@ 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 funsd-layoutlmv3 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5352
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- - Precision: 0.8892
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- - Recall: 0.9170
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- - F1: 0.9029
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- - Accuracy: 0.8690
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  ## Model description
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@@ -79,16 +79,16 @@ 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 | 1.33 | 100 | 0.7740 | 0.7470 | 0.8127 | 0.7785 | 0.7037 |
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- | No log | 2.67 | 200 | 0.5483 | 0.8111 | 0.8937 | 0.8504 | 0.7910 |
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- | No log | 4.0 | 300 | 0.4411 | 0.8400 | 0.8813 | 0.8601 | 0.8492 |
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- | No log | 5.33 | 400 | 0.4512 | 0.8432 | 0.8952 | 0.8684 | 0.8499 |
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- | 0.5705 | 6.67 | 500 | 0.4541 | 0.8865 | 0.9195 | 0.9027 | 0.8652 |
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- | 0.5705 | 8.0 | 600 | 0.4939 | 0.8782 | 0.9165 | 0.8969 | 0.8625 |
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- | 0.5705 | 9.33 | 700 | 0.5152 | 0.8792 | 0.9151 | 0.8968 | 0.8572 |
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- | 0.5705 | 10.67 | 800 | 0.5299 | 0.8856 | 0.9116 | 0.8984 | 0.8663 |
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- | 0.5705 | 12.0 | 900 | 0.5162 | 0.8894 | 0.9185 | 0.9037 | 0.8697 |
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- | 0.1324 | 13.33 | 1000 | 0.5352 | 0.8892 | 0.9170 | 0.9029 | 0.8690 |
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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: 0.9002457002457003
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  - name: Recall
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  type: recall
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+ value: 0.9100844510680576
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  - name: F1
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  type: f1
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+ value: 0.9051383399209486
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8547486033519553
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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 funsd-layoutlmv3 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6194
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+ - Precision: 0.9002
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+ - Recall: 0.9101
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+ - F1: 0.9051
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+ - Accuracy: 0.8547
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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 | 1.33 | 100 | 0.6953 | 0.7761 | 0.8058 | 0.7906 | 0.7680 |
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+ | No log | 2.67 | 200 | 0.5117 | 0.8250 | 0.8808 | 0.8520 | 0.8290 |
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+ | No log | 4.0 | 300 | 0.5177 | 0.8397 | 0.8897 | 0.8640 | 0.8337 |
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+ | No log | 5.33 | 400 | 0.5165 | 0.8642 | 0.9106 | 0.8868 | 0.8509 |
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+ | 0.5653 | 6.67 | 500 | 0.5378 | 0.8735 | 0.9091 | 0.8909 | 0.8458 |
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+ | 0.5653 | 8.0 | 600 | 0.5698 | 0.8733 | 0.9111 | 0.8918 | 0.8482 |
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+ | 0.5653 | 9.33 | 700 | 0.5773 | 0.8934 | 0.9076 | 0.9004 | 0.8557 |
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+ | 0.5653 | 10.67 | 800 | 0.6073 | 0.8905 | 0.9006 | 0.8955 | 0.8520 |
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+ | 0.5653 | 12.0 | 900 | 0.6090 | 0.8940 | 0.9091 | 0.9015 | 0.8513 |
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+ | 0.1357 | 13.33 | 1000 | 0.6194 | 0.9002 | 0.9101 | 0.9051 | 0.8547 |
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  ### Framework versions
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