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layoutlm-funsd

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  1. README.md +27 -28
  2. config.json +4 -14
  3. logs/events.out.tfevents.1704347182.10f5fb2b08d4.4019.0 +3 -0
  4. logs/events.out.tfevents.1704347567.10f5fb2b08d4.4019.1 +3 -0
  5. logs/events.out.tfevents.1704349091.10f5fb2b08d4.16771.0 +3 -0
  6. logs/events.out.tfevents.1704349345.10f5fb2b08d4.16771.1 +3 -0
  7. logs/events.out.tfevents.1704349388.10f5fb2b08d4.16771.2 +3 -0
  8. logs/events.out.tfevents.1704351137.10f5fb2b08d4.16771.3 +3 -0
  9. logs/events.out.tfevents.1704353785.10f5fb2b08d4.35120.0 +3 -0
  10. logs/events.out.tfevents.1704353946.10f5fb2b08d4.35120.1 +3 -0
  11. logs/events.out.tfevents.1704355374.10f5fb2b08d4.43552.0 +3 -0
  12. model.safetensors +2 -2
  13. training_args.bin +2 -2
README.md CHANGED
@@ -13,16 +13,15 @@ should probably proofread and complete it, then remove this comment. -->
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  # layoutlm-funsd
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16
- This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
17
  It achieves the following results on the evaluation set:
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- - Loss: 0.6988
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- - Answer: {'precision': 0.8288393903868698, 'recall': 0.8739184177997528, 'f1': 0.8507821901323707, 'number': 809}
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- - Header: {'precision': 0.41379310344827586, 'recall': 0.3025210084033613, 'f1': 0.34951456310679613, 'number': 119}
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- - Question: {'precision': 0.8503521126760564, 'recall': 0.9070422535211268, 'f1': 0.8777828259881872, 'number': 1065}
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- - Overall Precision: 0.8232
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- - Overall Recall: 0.8575
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- - Overall F1: 0.8400
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- - Overall Accuracy: 0.7993
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  ## Model description
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@@ -42,7 +41,7 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 3e-05
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- - train_batch_size: 16
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  - eval_batch_size: 8
47
  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
@@ -52,28 +51,28 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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- | 1.7803 | 1.0 | 10 | 1.5453 | {'precision': 0.0660377358490566, 'recall': 0.034610630407911, 'f1': 0.04541768045417681, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5203007518796993, 'recall': 0.3248826291079812, 'f1': 0.39999999999999997, 'number': 1065} | 0.3434 | 0.1877 | 0.2427 | 0.3654 |
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- | 1.3705 | 2.0 | 20 | 1.1973 | {'precision': 0.3941048034934498, 'recall': 0.446229913473424, 'f1': 0.41855072463768117, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4498360655737705, 'recall': 0.644131455399061, 'f1': 0.5297297297297298, 'number': 1065} | 0.4289 | 0.5253 | 0.4723 | 0.5669 |
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- | 1.0151 | 3.0 | 30 | 0.8981 | {'precision': 0.5953051643192488, 'recall': 0.7836835599505563, 'f1': 0.6766275346851655, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.7161234991423671, 'recall': 0.784037558685446, 'f1': 0.7485432541461228, 'number': 1065} | 0.6570 | 0.7371 | 0.6947 | 0.7115 |
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- | 0.7616 | 4.0 | 40 | 0.7858 | {'precision': 0.71, 'recall': 0.7898640296662547, 'f1': 0.7478057343475717, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.7176656151419558, 'recall': 0.8544600938967136, 'f1': 0.7801114444920704, 'number': 1065} | 0.7038 | 0.7772 | 0.7387 | 0.7454 |
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- | 0.6139 | 5.0 | 50 | 0.7552 | {'precision': 0.7357222844344905, 'recall': 0.8121137206427689, 'f1': 0.7720329024676851, 'number': 809} | {'precision': 0.15384615384615385, 'recall': 0.06722689075630252, 'f1': 0.0935672514619883, 'number': 119} | {'precision': 0.7778730703259005, 'recall': 0.8516431924882629, 'f1': 0.8130883012102196, 'number': 1065} | 0.7447 | 0.7888 | 0.7661 | 0.7477 |
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- | 0.5158 | 6.0 | 60 | 0.7328 | {'precision': 0.7719907407407407, 'recall': 0.8244746600741656, 'f1': 0.7973699940227136, 'number': 809} | {'precision': 0.19148936170212766, 'recall': 0.15126050420168066, 'f1': 0.16901408450704225, 'number': 119} | {'precision': 0.7967687074829932, 'recall': 0.8798122065727699, 'f1': 0.8362338241856315, 'number': 1065} | 0.7601 | 0.8138 | 0.7860 | 0.7524 |
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- | 0.4543 | 7.0 | 70 | 0.6304 | {'precision': 0.7913832199546486, 'recall': 0.8627935723114957, 'f1': 0.8255470136014192, 'number': 809} | {'precision': 0.32, 'recall': 0.20168067226890757, 'f1': 0.24742268041237112, 'number': 119} | {'precision': 0.7885245901639344, 'recall': 0.9032863849765258, 'f1': 0.8420131291028445, 'number': 1065} | 0.7735 | 0.8450 | 0.8077 | 0.7942 |
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- | 0.4024 | 8.0 | 80 | 0.7057 | {'precision': 0.7877412031782066, 'recall': 0.857849196538937, 'f1': 0.8213017751479289, 'number': 809} | {'precision': 0.3548387096774194, 'recall': 0.2773109243697479, 'f1': 0.3113207547169811, 'number': 119} | {'precision': 0.8283450704225352, 'recall': 0.8835680751173709, 'f1': 0.8550658791458428, 'number': 1065} | 0.7905 | 0.8369 | 0.8131 | 0.7727 |
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- | 0.3486 | 9.0 | 90 | 0.7059 | {'precision': 0.8018433179723502, 'recall': 0.8603213844252163, 'f1': 0.8300536672629696, 'number': 809} | {'precision': 0.43209876543209874, 'recall': 0.29411764705882354, 'f1': 0.35, 'number': 119} | {'precision': 0.8414526129317981, 'recall': 0.892018779342723, 'f1': 0.8659981768459435, 'number': 1065} | 0.8090 | 0.8435 | 0.8258 | 0.7992 |
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- | 0.3321 | 10.0 | 100 | 0.6924 | {'precision': 0.8339307048984468, 'recall': 0.8627935723114957, 'f1': 0.8481166464155528, 'number': 809} | {'precision': 0.44871794871794873, 'recall': 0.29411764705882354, 'f1': 0.3553299492385787, 'number': 119} | {'precision': 0.8396143733567046, 'recall': 0.8995305164319248, 'f1': 0.8685403445149591, 'number': 1065} | 0.8225 | 0.8485 | 0.8353 | 0.8014 |
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- | 0.2968 | 11.0 | 110 | 0.6866 | {'precision': 0.8399518652226233, 'recall': 0.8627935723114957, 'f1': 0.8512195121951219, 'number': 809} | {'precision': 0.3829787234042553, 'recall': 0.3025210084033613, 'f1': 0.3380281690140845, 'number': 119} | {'precision': 0.8458844133099825, 'recall': 0.9070422535211268, 'f1': 0.875396465790666, 'number': 1065} | 0.8224 | 0.8530 | 0.8374 | 0.7971 |
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- | 0.2694 | 12.0 | 120 | 0.6827 | {'precision': 0.8235294117647058, 'recall': 0.8825710754017305, 'f1': 0.8520286396181385, 'number': 809} | {'precision': 0.3977272727272727, 'recall': 0.29411764705882354, 'f1': 0.3381642512077294, 'number': 119} | {'precision': 0.8526785714285714, 'recall': 0.8967136150234741, 'f1': 0.8741418764302058, 'number': 1065} | 0.8212 | 0.8550 | 0.8378 | 0.8146 |
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- | 0.2609 | 13.0 | 130 | 0.6972 | {'precision': 0.8253223915592028, 'recall': 0.8702101359703337, 'f1': 0.8471720818291215, 'number': 809} | {'precision': 0.3564356435643564, 'recall': 0.3025210084033613, 'f1': 0.32727272727272727, 'number': 119} | {'precision': 0.8433945756780402, 'recall': 0.9051643192488263, 'f1': 0.8731884057971016, 'number': 1065} | 0.8126 | 0.8550 | 0.8333 | 0.8083 |
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- | 0.256 | 14.0 | 140 | 0.7048 | {'precision': 0.822637106184364, 'recall': 0.8714462299134734, 'f1': 0.8463385354141656, 'number': 809} | {'precision': 0.4069767441860465, 'recall': 0.29411764705882354, 'f1': 0.34146341463414637, 'number': 119} | {'precision': 0.8466312056737588, 'recall': 0.8967136150234741, 'f1': 0.870953032375741, 'number': 1065} | 0.8184 | 0.8505 | 0.8342 | 0.8137 |
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- | 0.2499 | 15.0 | 150 | 0.6988 | {'precision': 0.8288393903868698, 'recall': 0.8739184177997528, 'f1': 0.8507821901323707, 'number': 809} | {'precision': 0.41379310344827586, 'recall': 0.3025210084033613, 'f1': 0.34951456310679613, 'number': 119} | {'precision': 0.8503521126760564, 'recall': 0.9070422535211268, 'f1': 0.8777828259881872, 'number': 1065} | 0.8232 | 0.8575 | 0.8400 | 0.7993 |
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  ### Framework versions
75
 
76
  - Transformers 4.36.2
77
- - Pytorch 2.1.0+cu121
78
  - Datasets 2.16.1
79
  - Tokenizers 0.15.0
 
13
 
14
  # layoutlm-funsd
15
 
16
+ This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
17
  It achieves the following results on the evaluation set:
18
+ - Loss: 0.0754
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+ - Ignal: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}
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+ - Oise: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12}
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+ - Overall Precision: 0.0
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+ - Overall Recall: 0.0
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+ - Overall F1: 0.0
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+ - Overall Accuracy: 0.9670
 
25
 
26
  ## Model description
27
 
 
41
 
42
  The following hyperparameters were used during training:
43
  - learning_rate: 3e-05
44
+ - train_batch_size: 8
45
  - eval_batch_size: 8
46
  - seed: 42
47
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 
51
 
52
  ### Training results
53
 
54
+ | Training Loss | Epoch | Step | Validation Loss | Ignal | Oise | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
55
+ |:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 0.7198 | 1.0 | 1 | 0.7152 | {'precision': 0.010416666666666666, 'recall': 0.09090909090909091, 'f1': 0.018691588785046728, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0052 | 0.0435 | 0.0093 | 0.5024 |
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+ | 0.7121 | 2.0 | 2 | 0.7152 | {'precision': 0.010416666666666666, 'recall': 0.09090909090909091, 'f1': 0.018691588785046728, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0052 | 0.0435 | 0.0093 | 0.5024 |
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+ | 0.7191 | 3.0 | 3 | 0.4802 | {'precision': 0.045454545454545456, 'recall': 0.09090909090909091, 'f1': 0.060606060606060615, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0222 | 0.0435 | 0.0294 | 0.9245 |
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+ | 0.4799 | 4.0 | 4 | 0.3268 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9646 |
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+ | 0.3263 | 5.0 | 5 | 0.2246 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
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+ | 0.2269 | 6.0 | 6 | 0.1598 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
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+ | 0.1625 | 7.0 | 7 | 0.1227 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
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+ | 0.1246 | 8.0 | 8 | 0.1030 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
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+ | 0.1042 | 9.0 | 9 | 0.0937 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
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+ | 0.0942 | 10.0 | 10 | 0.0892 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
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+ | 0.0888 | 11.0 | 11 | 0.0861 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
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+ | 0.0834 | 12.0 | 12 | 0.0832 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
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+ | 0.0768 | 13.0 | 13 | 0.0805 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
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+ | 0.0745 | 14.0 | 14 | 0.0778 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
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+ | 0.071 | 15.0 | 15 | 0.0754 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0 | 0.0 | 0.0 | 0.9670 |
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72
 
73
  ### Framework versions
74
 
75
  - Transformers 4.36.2
76
+ - Pytorch 2.1.2+cu121
77
  - Datasets 2.16.1
78
  - Tokenizers 0.15.0
config.json CHANGED
@@ -8,24 +8,14 @@
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  "hidden_dropout_prob": 0.1,
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  "hidden_size": 768,
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  "id2label": {
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- "0": "O",
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- "1": "B-HEADER",
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- "2": "I-HEADER",
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- "3": "B-QUESTION",
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- "4": "I-QUESTION",
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- "5": "B-ANSWER",
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- "6": "I-ANSWER"
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  },
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  "initializer_range": 0.02,
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  "intermediate_size": 3072,
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  "label2id": {
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- "B-ANSWER": 5,
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- "B-HEADER": 1,
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- "B-QUESTION": 3,
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- "I-ANSWER": 6,
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- "I-HEADER": 2,
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- "I-QUESTION": 4,
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- "O": 0
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  },
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  "layer_norm_eps": 1e-12,
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  "max_2d_position_embeddings": 1024,
 
8
  "hidden_dropout_prob": 0.1,
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  "hidden_size": 768,
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  "id2label": {
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13
  },
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  "initializer_range": 0.02,
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  "intermediate_size": 3072,
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  "label2id": {
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+ "noise": 0,
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+ "signal": 1
 
 
 
 
 
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  },
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  "layer_norm_eps": 1e-12,
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  "max_2d_position_embeddings": 1024,
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