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mp-02
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layoutlmv3-finetuned-funsd

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update model card README.md

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@@ -20,16 +20,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.8553875236294896
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  - name: Recall
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  type: recall
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- value: 0.905
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  - name: F1
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  type: f1
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- value: 0.8794946550048591
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  - name: Accuracy
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  type: accuracy
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- value: 0.833371612310519
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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
@@ -39,11 +39,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [layoutlmv3](https://huggingface.co/layoutlmv3) on the mp-02/funsd dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5784
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- - Precision: 0.8554
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- - Recall: 0.905
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- - F1: 0.8795
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- - Accuracy: 0.8334
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  ## Model description
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@@ -63,7 +63,7 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 1e-05
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- - train_batch_size: 4
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  - eval_batch_size: 16
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 0.66 | 25 | 1.3511 | 0.3301 | 0.3585 | 0.3437 | 0.5721 |
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- | No log | 1.32 | 50 | 0.9059 | 0.6965 | 0.7515 | 0.7229 | 0.7615 |
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- | No log | 1.97 | 75 | 0.7164 | 0.7613 | 0.831 | 0.7946 | 0.7796 |
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- | No log | 2.63 | 100 | 0.6393 | 0.7947 | 0.8575 | 0.8249 | 0.7993 |
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- | No log | 3.29 | 125 | 0.5756 | 0.8138 | 0.87 | 0.8410 | 0.8104 |
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- | No log | 3.95 | 150 | 0.5508 | 0.8197 | 0.884 | 0.8506 | 0.8323 |
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- | No log | 4.61 | 175 | 0.5458 | 0.8325 | 0.8895 | 0.8600 | 0.8328 |
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- | No log | 5.26 | 200 | 0.5740 | 0.8234 | 0.8765 | 0.8491 | 0.8266 |
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- | No log | 5.92 | 225 | 0.5719 | 0.8532 | 0.8895 | 0.8710 | 0.8361 |
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- | No log | 6.58 | 250 | 0.5436 | 0.8439 | 0.9055 | 0.8736 | 0.8264 |
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- | No log | 7.24 | 275 | 0.5714 | 0.8520 | 0.9065 | 0.8784 | 0.8290 |
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- | No log | 7.89 | 300 | 0.5853 | 0.8560 | 0.9035 | 0.8791 | 0.8281 |
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- | No log | 8.55 | 325 | 0.5702 | 0.8578 | 0.905 | 0.8808 | 0.8390 |
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- | No log | 9.21 | 350 | 0.5667 | 0.8552 | 0.901 | 0.8775 | 0.8419 |
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- | No log | 9.87 | 375 | 0.5793 | 0.8552 | 0.9035 | 0.8787 | 0.8338 |
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- | No log | 10.53 | 400 | 0.5784 | 0.8554 | 0.905 | 0.8795 | 0.8334 |
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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: 0.8746976294146106
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  - name: Recall
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  type: recall
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+ value: 0.904
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  - name: F1
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  type: f1
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+ value: 0.8891074502089993
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8368167202572347
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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 [layoutlmv3](https://huggingface.co/layoutlmv3) on the mp-02/funsd dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6541
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+ - Precision: 0.8747
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+ - Recall: 0.904
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+ - F1: 0.8891
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+ - Accuracy: 0.8368
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 1e-05
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+ - train_batch_size: 6
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  - eval_batch_size: 16
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 
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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 | 25 | 1.2831 | 0.4033 | 0.4795 | 0.4381 | 0.6092 |
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+ | No log | 2.0 | 50 | 0.8178 | 0.7266 | 0.7935 | 0.7586 | 0.7748 |
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+ | No log | 3.0 | 75 | 0.6843 | 0.7951 | 0.8345 | 0.8143 | 0.7990 |
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+ | No log | 4.0 | 100 | 0.6317 | 0.8024 | 0.861 | 0.8307 | 0.8161 |
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+ | No log | 5.0 | 125 | 0.5964 | 0.8260 | 0.897 | 0.8600 | 0.8234 |
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+ | No log | 6.0 | 150 | 0.6050 | 0.8204 | 0.87 | 0.8445 | 0.8207 |
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+ | No log | 7.0 | 175 | 0.6281 | 0.8203 | 0.8765 | 0.8475 | 0.8168 |
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+ | No log | 8.0 | 200 | 0.6228 | 0.8449 | 0.8985 | 0.8709 | 0.8235 |
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+ | No log | 9.0 | 225 | 0.6213 | 0.8345 | 0.88 | 0.8567 | 0.8266 |
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+ | No log | 10.0 | 250 | 0.6173 | 0.8450 | 0.897 | 0.8702 | 0.8357 |
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+ | No log | 11.0 | 275 | 0.6476 | 0.8388 | 0.8895 | 0.8634 | 0.8299 |
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+ | No log | 12.0 | 300 | 0.6359 | 0.8584 | 0.8945 | 0.8761 | 0.8382 |
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+ | No log | 13.0 | 325 | 0.6469 | 0.8759 | 0.907 | 0.8912 | 0.8395 |
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+ | No log | 14.0 | 350 | 0.6510 | 0.8729 | 0.9035 | 0.8880 | 0.8373 |
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+ | No log | 15.0 | 375 | 0.6555 | 0.8656 | 0.902 | 0.8834 | 0.8354 |
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+ | No log | 16.0 | 400 | 0.6541 | 0.8747 | 0.904 | 0.8891 | 0.8368 |
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  ### Framework versions