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MSAMTB
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lilt-en-funsd-layoutlmv3

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Training in progress, step 200

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  1. README.md +22 -23
  2. logs/events.out.tfevents.1719324686.Allianz-Editique.1964.0 +2 -2
  3. logs/events.out.tfevents.1719327221.Allianz-Editique.2290.0 +3 -0
  4. logs/events.out.tfevents.1719343104.Allianz-Editique.3019.0 +3 -0
  5. logs/events.out.tfevents.1719343147.Allianz-Editique.3155.0 +3 -0
  6. logs/events.out.tfevents.1719343178.Allianz-Editique.3263.0 +3 -0
  7. logs/events.out.tfevents.1719343262.Allianz-Editique.3404.0 +3 -0
  8. logs/events.out.tfevents.1719343334.Allianz-Editique.3488.0 +3 -0
  9. logs/events.out.tfevents.1719343371.Allianz-Editique.3545.0 +3 -0
  10. logs/events.out.tfevents.1719343400.Allianz-Editique.3601.0 +3 -0
  11. model.safetensors +1 -1
  12. training_args.bin +1 -1
README.md CHANGED
@@ -15,14 +15,14 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on an unknown dataset.
17
  It achieves the following results on the evaluation set:
18
- - Loss: 1.6598
19
- - Answer: {'precision': 0.8569780853517878, 'recall': 0.9094247246022031, 'f1': 0.8824228028503563, 'number': 817}
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- - Header: {'precision': 0.616822429906542, 'recall': 0.5546218487394958, 'f1': 0.5840707964601769, 'number': 119}
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- - Question: {'precision': 0.8877917414721723, 'recall': 0.9182915506035283, 'f1': 0.9027841168416247, 'number': 1077}
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- - Overall Precision: 0.8611
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- - Overall Recall: 0.8932
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- - Overall F1: 0.8769
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- - Overall Accuracy: 0.8110
26
 
27
  ## Model description
28
 
@@ -45,7 +45,6 @@ The following hyperparameters were used during training:
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
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- - distributed_type: multi-GPU
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - training_steps: 2500
@@ -53,20 +52,20 @@ The following hyperparameters were used during training:
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  ### Training results
55
 
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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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- | 0.3825 | 10.5263 | 200 | 1.0337 | {'precision': 0.8096774193548387, 'recall': 0.9216646266829865, 'f1': 0.8620492272467086, 'number': 817} | {'precision': 0.57, 'recall': 0.4789915966386555, 'f1': 0.5205479452054794, 'number': 119} | {'precision': 0.8878591288229842, 'recall': 0.8895078922934077, 'f1': 0.888682745825603, 'number': 1077} | 0.8383 | 0.8783 | 0.8578 | 0.7937 |
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- | 0.0479 | 21.0526 | 400 | 1.2331 | {'precision': 0.8652482269503546, 'recall': 0.8959608323133414, 'f1': 0.8803367408298255, 'number': 817} | {'precision': 0.5378787878787878, 'recall': 0.5966386554621849, 'f1': 0.5657370517928287, 'number': 119} | {'precision': 0.8864042933810375, 'recall': 0.9201485608170845, 'f1': 0.9029612756264238, 'number': 1077} | 0.8559 | 0.8912 | 0.8732 | 0.8016 |
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- | 0.0135 | 31.5789 | 600 | 1.3752 | {'precision': 0.8589894242068156, 'recall': 0.8947368421052632, 'f1': 0.8764988009592326, 'number': 817} | {'precision': 0.47305389221556887, 'recall': 0.6638655462184874, 'f1': 0.5524475524475525, 'number': 119} | {'precision': 0.8785388127853881, 'recall': 0.89322191272052, 'f1': 0.8858195211786372, 'number': 1077} | 0.8386 | 0.8803 | 0.8589 | 0.8054 |
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- | 0.0076 | 42.1053 | 800 | 1.3688 | {'precision': 0.8801955990220048, 'recall': 0.8812729498164015, 'f1': 0.8807339449541285, 'number': 817} | {'precision': 0.5546875, 'recall': 0.5966386554621849, 'f1': 0.5748987854251012, 'number': 119} | {'precision': 0.8756567425569177, 'recall': 0.9285051067780873, 'f1': 0.9013068949977467, 'number': 1077} | 0.8578 | 0.8897 | 0.8734 | 0.8028 |
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- | 0.003 | 52.6316 | 1000 | 1.6920 | {'precision': 0.8350398179749715, 'recall': 0.8984088127294981, 'f1': 0.8655660377358491, 'number': 817} | {'precision': 0.5354330708661418, 'recall': 0.5714285714285714, 'f1': 0.5528455284552845, 'number': 119} | {'precision': 0.8950381679389313, 'recall': 0.8709377901578459, 'f1': 0.8828235294117648, 'number': 1077} | 0.8471 | 0.8644 | 0.8557 | 0.7898 |
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- | 0.0028 | 63.1579 | 1200 | 1.6114 | {'precision': 0.8316268486916951, 'recall': 0.8947368421052632, 'f1': 0.8620283018867924, 'number': 817} | {'precision': 0.527027027027027, 'recall': 0.6554621848739496, 'f1': 0.5842696629213482, 'number': 119} | {'precision': 0.8915094339622641, 'recall': 0.8774373259052924, 'f1': 0.8844174075807205, 'number': 1077} | 0.8404 | 0.8713 | 0.8556 | 0.7919 |
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- | 0.0011 | 73.6842 | 1400 | 1.5815 | {'precision': 0.8320180383314544, 'recall': 0.9033047735618115, 'f1': 0.8661971830985915, 'number': 817} | {'precision': 0.5925925925925926, 'recall': 0.5378151260504201, 'f1': 0.5638766519823789, 'number': 119} | {'precision': 0.8895927601809954, 'recall': 0.9127205199628597, 'f1': 0.9010082493125573, 'number': 1077} | 0.85 | 0.8867 | 0.8680 | 0.8007 |
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- | 0.0008 | 84.2105 | 1600 | 1.6211 | {'precision': 0.8632075471698113, 'recall': 0.8959608323133414, 'f1': 0.8792792792792793, 'number': 817} | {'precision': 0.6346153846153846, 'recall': 0.5546218487394958, 'f1': 0.5919282511210763, 'number': 119} | {'precision': 0.8871111111111111, 'recall': 0.9266480965645311, 'f1': 0.9064486830154405, 'number': 1077} | 0.8647 | 0.8922 | 0.8782 | 0.8102 |
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- | 0.0009 | 94.7368 | 1800 | 1.5792 | {'precision': 0.8789346246973365, 'recall': 0.8886168910648715, 'f1': 0.8837492391965917, 'number': 817} | {'precision': 0.5916666666666667, 'recall': 0.5966386554621849, 'f1': 0.5941422594142259, 'number': 119} | {'precision': 0.8811188811188811, 'recall': 0.935933147632312, 'f1': 0.9076992345790185, 'number': 1077} | 0.8636 | 0.8967 | 0.8798 | 0.8261 |
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- | 0.0005 | 105.2632 | 2000 | 1.6499 | {'precision': 0.8393665158371041, 'recall': 0.9082007343941249, 'f1': 0.8724279835390947, 'number': 817} | {'precision': 0.6036036036036037, 'recall': 0.5630252100840336, 'f1': 0.582608695652174, 'number': 119} | {'precision': 0.8933454876937101, 'recall': 0.9099350046425255, 'f1': 0.9015639374425023, 'number': 1077} | 0.8552 | 0.8887 | 0.8716 | 0.8086 |
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- | 0.0003 | 115.7895 | 2200 | 1.6743 | {'precision': 0.849942726231386, 'recall': 0.9082007343941249, 'f1': 0.8781065088757396, 'number': 817} | {'precision': 0.6111111111111112, 'recall': 0.5546218487394958, 'f1': 0.5814977973568282, 'number': 119} | {'precision': 0.8869801084990958, 'recall': 0.9108635097493036, 'f1': 0.8987631699496106, 'number': 1077} | 0.8572 | 0.8887 | 0.8727 | 0.8077 |
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- | 0.0002 | 126.3158 | 2400 | 1.6598 | {'precision': 0.8569780853517878, 'recall': 0.9094247246022031, 'f1': 0.8824228028503563, 'number': 817} | {'precision': 0.616822429906542, 'recall': 0.5546218487394958, 'f1': 0.5840707964601769, 'number': 119} | {'precision': 0.8877917414721723, 'recall': 0.9182915506035283, 'f1': 0.9027841168416247, 'number': 1077} | 0.8611 | 0.8932 | 0.8769 | 0.8110 |
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  ### Framework versions
 
15
 
16
  This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on an unknown dataset.
17
  It achieves the following results on the evaluation set:
18
+ - Loss: 1.6323
19
+ - Answer: {'precision': 0.8683901292596945, 'recall': 0.9045287637698899, 'f1': 0.8860911270983215, 'number': 817}
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+ - Header: {'precision': 0.6095238095238096, 'recall': 0.5378151260504201, 'f1': 0.5714285714285715, 'number': 119}
21
+ - Question: {'precision': 0.90063233965673, 'recall': 0.9257195914577531, 'f1': 0.913003663003663, 'number': 1077}
22
+ - Overall Precision: 0.8725
23
+ - Overall Recall: 0.8942
24
+ - Overall F1: 0.8832
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+ - Overall Accuracy: 0.8071
26
 
27
  ## Model description
28
 
 
45
  - train_batch_size: 8
46
  - eval_batch_size: 8
47
  - seed: 42
 
48
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
49
  - lr_scheduler_type: linear
50
  - training_steps: 2500
 
52
 
53
  ### Training results
54
 
55
+ | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
56
+ |:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 0.4052 | 10.5263 | 200 | 1.0646 | {'precision': 0.8046709129511678, 'recall': 0.9277845777233782, 'f1': 0.861853325753269, 'number': 817} | {'precision': 0.5803571428571429, 'recall': 0.5462184873949579, 'f1': 0.5627705627705628, 'number': 119} | {'precision': 0.8797061524334252, 'recall': 0.8895078922934077, 'f1': 0.8845798707294553, 'number': 1077} | 0.8311 | 0.8847 | 0.8571 | 0.7850 |
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+ | 0.0474 | 21.0526 | 400 | 1.2300 | {'precision': 0.8500590318772137, 'recall': 0.8812729498164015, 'f1': 0.8653846153846153, 'number': 817} | {'precision': 0.5454545454545454, 'recall': 0.6554621848739496, 'f1': 0.5954198473282444, 'number': 119} | {'precision': 0.8798908098271155, 'recall': 0.8978644382544104, 'f1': 0.8887867647058824, 'number': 1077} | 0.8449 | 0.8768 | 0.8606 | 0.8026 |
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+ | 0.0127 | 31.5789 | 600 | 1.5767 | {'precision': 0.8359728506787331, 'recall': 0.9045287637698899, 'f1': 0.8689006466784246, 'number': 817} | {'precision': 0.5583333333333333, 'recall': 0.5630252100840336, 'f1': 0.5606694560669456, 'number': 119} | {'precision': 0.8940397350993378, 'recall': 0.8774373259052924, 'f1': 0.8856607310215557, 'number': 1077} | 0.8496 | 0.8698 | 0.8596 | 0.7835 |
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+ | 0.0085 | 42.1053 | 800 | 1.3875 | {'precision': 0.833710407239819, 'recall': 0.9020807833537332, 'f1': 0.8665490887713109, 'number': 817} | {'precision': 0.6363636363636364, 'recall': 0.5294117647058824, 'f1': 0.5779816513761468, 'number': 119} | {'precision': 0.8825654923215899, 'recall': 0.9071494893221913, 'f1': 0.8946886446886446, 'number': 1077} | 0.8502 | 0.8828 | 0.8662 | 0.8072 |
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+ | 0.0058 | 52.6316 | 1000 | 1.4794 | {'precision': 0.8272017837235228, 'recall': 0.9082007343941249, 'f1': 0.8658109684947491, 'number': 817} | {'precision': 0.5203252032520326, 'recall': 0.5378151260504201, 'f1': 0.5289256198347108, 'number': 119} | {'precision': 0.8829981718464351, 'recall': 0.8969359331476323, 'f1': 0.889912482726854, 'number': 1077} | 0.8382 | 0.8803 | 0.8587 | 0.7964 |
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+ | 0.0038 | 63.1579 | 1200 | 1.5286 | {'precision': 0.8443935926773455, 'recall': 0.9033047735618115, 'f1': 0.872856298048492, 'number': 817} | {'precision': 0.625, 'recall': 0.5042016806722689, 'f1': 0.5581395348837209, 'number': 119} | {'precision': 0.8991674375578168, 'recall': 0.9025069637883009, 'f1': 0.9008341056533827, 'number': 1077} | 0.8630 | 0.8793 | 0.8711 | 0.8084 |
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+ | 0.0023 | 73.6842 | 1400 | 1.6443 | {'precision': 0.8725146198830409, 'recall': 0.9130966952264382, 'f1': 0.8923444976076554, 'number': 817} | {'precision': 0.5, 'recall': 0.5210084033613446, 'f1': 0.5102880658436215, 'number': 119} | {'precision': 0.8906955736224029, 'recall': 0.9155060352831941, 'f1': 0.9029304029304029, 'number': 1077} | 0.8600 | 0.8912 | 0.8753 | 0.8054 |
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+ | 0.0012 | 84.2105 | 1600 | 1.6379 | {'precision': 0.8404977375565611, 'recall': 0.9094247246022031, 'f1': 0.8736037624926513, 'number': 817} | {'precision': 0.6224489795918368, 'recall': 0.5126050420168067, 'f1': 0.5622119815668203, 'number': 119} | {'precision': 0.8944444444444445, 'recall': 0.8969359331476323, 'f1': 0.8956884561891516, 'number': 1077} | 0.8584 | 0.8793 | 0.8687 | 0.8008 |
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+ | 0.0005 | 94.7368 | 1800 | 1.6798 | {'precision': 0.8450057405281286, 'recall': 0.9008567931456548, 'f1': 0.8720379146919431, 'number': 817} | {'precision': 0.6534653465346535, 'recall': 0.5546218487394958, 'f1': 0.6000000000000001, 'number': 119} | {'precision': 0.8886861313868614, 'recall': 0.904363974001857, 'f1': 0.8964565117349288, 'number': 1077} | 0.8588 | 0.8823 | 0.8704 | 0.7988 |
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+ | 0.0004 | 105.2632 | 2000 | 1.6804 | {'precision': 0.8596491228070176, 'recall': 0.8996328029375765, 'f1': 0.8791866028708135, 'number': 817} | {'precision': 0.5203252032520326, 'recall': 0.5378151260504201, 'f1': 0.5289256198347108, 'number': 119} | {'precision': 0.8869801084990958, 'recall': 0.9108635097493036, 'f1': 0.8987631699496106, 'number': 1077} | 0.8541 | 0.8843 | 0.8689 | 0.8032 |
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+ | 0.0003 | 115.7895 | 2200 | 1.6352 | {'precision': 0.8713105076741441, 'recall': 0.9033047735618115, 'f1': 0.8870192307692307, 'number': 817} | {'precision': 0.6153846153846154, 'recall': 0.5378151260504201, 'f1': 0.5739910313901345, 'number': 119} | {'precision': 0.8938848920863309, 'recall': 0.9229340761374187, 'f1': 0.9081772498857925, 'number': 1077} | 0.8706 | 0.8922 | 0.8813 | 0.8066 |
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+ | 0.0002 | 126.3158 | 2400 | 1.6323 | {'precision': 0.8683901292596945, 'recall': 0.9045287637698899, 'f1': 0.8860911270983215, 'number': 817} | {'precision': 0.6095238095238096, 'recall': 0.5378151260504201, 'f1': 0.5714285714285715, 'number': 119} | {'precision': 0.90063233965673, 'recall': 0.9257195914577531, 'f1': 0.913003663003663, 'number': 1077} | 0.8725 | 0.8942 | 0.8832 | 0.8071 |
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