File size: 8,509 Bytes
4eba019 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
---
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layoutlm-funsd
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlm-funsd
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3725
- Answer: {'precision': 0.07982261640798226, 'recall': 0.08899876390605686, 'f1': 0.0841613091759205, 'number': 809}
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
- Question: {'precision': 0.4174242424242424, 'recall': 0.5173708920187794, 'f1': 0.46205450733752623, 'number': 1065}
- Overall Precision: 0.2804
- Overall Recall: 0.3126
- Overall F1: 0.2956
- Overall Accuracy: 0.5437
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8773 | 1.0 | 10 | 1.8489 | {'precision': 0.00547645125958379, 'recall': 0.006180469715698393, 'f1': 0.005807200929152149, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.04874446085672083, 'recall': 0.030985915492957747, 'f1': 0.03788748564867968, 'number': 1065} | 0.0227 | 0.0191 | 0.0207 | 0.2819 |
| 1.807 | 2.0 | 20 | 1.7831 | {'precision': 0.005925925925925926, 'recall': 0.004944375772558714, 'f1': 0.005390835579514824, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.06716417910447761, 'recall': 0.03380281690140845, 'f1': 0.04497189256714553, 'number': 1065} | 0.0327 | 0.0201 | 0.0249 | 0.2996 |
| 1.7516 | 3.0 | 30 | 1.7272 | {'precision': 0.0071633237822349575, 'recall': 0.006180469715698393, 'f1': 0.006635700066357001, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.10175438596491228, 'recall': 0.054460093896713614, 'f1': 0.0709480122324159, 'number': 1065} | 0.0496 | 0.0316 | 0.0386 | 0.3189 |
| 1.7057 | 4.0 | 40 | 1.6785 | {'precision': 0.012626262626262626, 'recall': 0.012360939431396786, 'f1': 0.012492192379762648, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.16886930983847284, 'recall': 0.107981220657277, 'f1': 0.13172966781214204, 'number': 1065} | 0.0849 | 0.0627 | 0.0721 | 0.3426 |
| 1.6571 | 5.0 | 50 | 1.6336 | {'precision': 0.016286644951140065, 'recall': 0.018541409147095178, 'f1': 0.017341040462427744, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2211764705882353, 'recall': 0.17652582159624414, 'f1': 0.19634464751958225, 'number': 1065} | 0.1146 | 0.1019 | 0.1079 | 0.3714 |
| 1.6219 | 6.0 | 60 | 1.5894 | {'precision': 0.03238095238095238, 'recall': 0.042027194066749075, 'f1': 0.036578805809575045, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.26129666011787817, 'recall': 0.24976525821596243, 'f1': 0.2554008641382621, 'number': 1065} | 0.1451 | 0.1505 | 0.1477 | 0.4028 |
| 1.5748 | 7.0 | 70 | 1.5484 | {'precision': 0.03796296296296296, 'recall': 0.05067985166872682, 'f1': 0.04340921122286924, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.28073394495412846, 'recall': 0.28732394366197184, 'f1': 0.28399071925754066, 'number': 1065} | 0.1599 | 0.1741 | 0.1667 | 0.4319 |
| 1.5387 | 8.0 | 80 | 1.5098 | {'precision': 0.044036697247706424, 'recall': 0.059332509270704575, 'f1': 0.05055292259083728, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.30583333333333335, 'recall': 0.34460093896713617, 'f1': 0.3240618101545254, 'number': 1065} | 0.1812 | 0.2082 | 0.1938 | 0.4623 |
| 1.5004 | 9.0 | 90 | 1.4753 | {'precision': 0.05149812734082397, 'recall': 0.06798516687268233, 'f1': 0.05860415556739478, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3378812199036918, 'recall': 0.39530516431924884, 'f1': 0.36434443963652097, 'number': 1065} | 0.2057 | 0.2388 | 0.2210 | 0.4887 |
| 1.4659 | 10.0 | 100 | 1.4462 | {'precision': 0.058823529411764705, 'recall': 0.0754017305315204, 'f1': 0.06608884073672806, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3586530931871574, 'recall': 0.4300469483568075, 'f1': 0.39111870196413323, 'number': 1065} | 0.2243 | 0.2604 | 0.2410 | 0.5046 |
| 1.4314 | 11.0 | 110 | 1.4207 | {'precision': 0.06769230769230769, 'recall': 0.0815822002472188, 'f1': 0.07399103139013452, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.38271604938271603, 'recall': 0.46572769953051646, 'f1': 0.42016094875052945, 'number': 1065} | 0.2475 | 0.2820 | 0.2636 | 0.5184 |
| 1.4242 | 12.0 | 120 | 1.4003 | {'precision': 0.07203389830508475, 'recall': 0.08405438813349815, 'f1': 0.0775812892184826, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.40076628352490423, 'recall': 0.49107981220657276, 'f1': 0.4413502109704641, 'number': 1065} | 0.2628 | 0.2965 | 0.2786 | 0.5273 |
| 1.3939 | 13.0 | 130 | 1.3855 | {'precision': 0.07792207792207792, 'recall': 0.08899876390605686, 'f1': 0.0830929024812464, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.40953822861468586, 'recall': 0.507981220657277, 'f1': 0.45347862531433364, 'number': 1065} | 0.2731 | 0.3076 | 0.2893 | 0.5367 |
| 1.3837 | 14.0 | 140 | 1.3764 | {'precision': 0.08021978021978023, 'recall': 0.09023485784919653, 'f1': 0.08493310063990692, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.41635124905374715, 'recall': 0.5164319248826291, 'f1': 0.4610226320201173, 'number': 1065} | 0.2792 | 0.3126 | 0.2950 | 0.5410 |
| 1.3603 | 15.0 | 150 | 1.3725 | {'precision': 0.07982261640798226, 'recall': 0.08899876390605686, 'f1': 0.0841613091759205, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4174242424242424, 'recall': 0.5173708920187794, 'f1': 0.46205450733752623, 'number': 1065} | 0.2804 | 0.3126 | 0.2956 | 0.5437 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
|