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library_name: transformers
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6826
- Answer: {'precision': 0.7305524239007892, 'recall': 0.8009888751545118, 'f1': 0.7641509433962265, 'number': 809}
- Header: {'precision': 0.3643410852713178, 'recall': 0.3949579831932773, 'f1': 0.3790322580645162, 'number': 119}
- Question: {'precision': 0.7896613190730838, 'recall': 0.831924882629108, 'f1': 0.8102423411065386, 'number': 1065}
- Overall Precision: 0.7395
- Overall Recall: 0.7933
- Overall F1: 0.7654
- Overall Accuracy: 0.8177
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8197 | 1.0 | 10 | 1.6129 | {'precision': 0.02245508982035928, 'recall': 0.018541409147095178, 'f1': 0.020311442112389978, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.26639344262295084, 'recall': 0.18309859154929578, 'f1': 0.21702838063439067, 'number': 1065} | 0.15 | 0.1054 | 0.1238 | 0.3471 |
| 1.459 | 2.0 | 20 | 1.2609 | {'precision': 0.2609673790776153, 'recall': 0.2867737948084054, 'f1': 0.27326266195524146, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4169329073482428, 'recall': 0.49014084507042255, 'f1': 0.4505826499784203, 'number': 1065} | 0.3522 | 0.3783 | 0.3648 | 0.5855 |
| 1.105 | 3.0 | 30 | 0.9633 | {'precision': 0.48520710059171596, 'recall': 0.6081582200247219, 'f1': 0.53976961053209, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5333333333333333, 'recall': 0.676056338028169, 'f1': 0.5962732919254659, 'number': 1065} | 0.5107 | 0.6081 | 0.5552 | 0.7051 |
| 0.8404 | 4.0 | 40 | 0.8047 | {'precision': 0.5744274809160306, 'recall': 0.7441285537700866, 'f1': 0.6483575659666129, 'number': 809} | {'precision': 0.08333333333333333, 'recall': 0.03361344537815126, 'f1': 0.04790419161676646, 'number': 119} | {'precision': 0.635036496350365, 'recall': 0.7352112676056338, 'f1': 0.6814621409921671, 'number': 1065} | 0.5964 | 0.6969 | 0.6428 | 0.7578 |
| 0.6838 | 5.0 | 50 | 0.7318 | {'precision': 0.6411637931034483, 'recall': 0.7354758961681088, 'f1': 0.6850892343120323, 'number': 809} | {'precision': 0.2236842105263158, 'recall': 0.14285714285714285, 'f1': 0.17435897435897438, 'number': 119} | {'precision': 0.6717011128775835, 'recall': 0.7934272300469484, 'f1': 0.7275075333620319, 'number': 1065} | 0.6441 | 0.7311 | 0.6848 | 0.7834 |
| 0.5808 | 6.0 | 60 | 0.7147 | {'precision': 0.6506276150627615, 'recall': 0.7688504326328801, 'f1': 0.7048158640226628, 'number': 809} | {'precision': 0.32894736842105265, 'recall': 0.21008403361344538, 'f1': 0.25641025641025644, 'number': 119} | {'precision': 0.7051826677994902, 'recall': 0.7793427230046949, 'f1': 0.7404103479036575, 'number': 1065} | 0.6686 | 0.7411 | 0.7030 | 0.7825 |
| 0.5061 | 7.0 | 70 | 0.6761 | {'precision': 0.68, 'recall': 0.7775030902348579, 'f1': 0.7254901960784315, 'number': 809} | {'precision': 0.3274336283185841, 'recall': 0.31092436974789917, 'f1': 0.3189655172413793, 'number': 119} | {'precision': 0.7265692175408427, 'recall': 0.7934272300469484, 'f1': 0.7585278276481149, 'number': 1065} | 0.6865 | 0.7582 | 0.7206 | 0.7993 |
| 0.4467 | 8.0 | 80 | 0.6618 | {'precision': 0.676130389064143, 'recall': 0.7948084054388134, 'f1': 0.7306818181818182, 'number': 809} | {'precision': 0.27184466019417475, 'recall': 0.23529411764705882, 'f1': 0.2522522522522523, 'number': 119} | {'precision': 0.7289719626168224, 'recall': 0.8056338028169014, 'f1': 0.7653880463871543, 'number': 1065} | 0.6853 | 0.7672 | 0.7240 | 0.8040 |
| 0.399 | 9.0 | 90 | 0.6648 | {'precision': 0.6933911159263272, 'recall': 0.7911001236093943, 'f1': 0.7390300230946881, 'number': 809} | {'precision': 0.3103448275862069, 'recall': 0.3025210084033613, 'f1': 0.30638297872340425, 'number': 119} | {'precision': 0.7459366980325064, 'recall': 0.8187793427230047, 'f1': 0.7806624888093106, 'number': 1065} | 0.7011 | 0.7767 | 0.7370 | 0.8099 |
| 0.3777 | 10.0 | 100 | 0.6685 | {'precision': 0.7158962795941376, 'recall': 0.7849196538936959, 'f1': 0.7488207547169812, 'number': 809} | {'precision': 0.3162393162393162, 'recall': 0.31092436974789917, 'f1': 0.3135593220338983, 'number': 119} | {'precision': 0.7574978577549272, 'recall': 0.8300469483568075, 'f1': 0.7921146953405018, 'number': 1065} | 0.7167 | 0.7807 | 0.7474 | 0.8111 |
| 0.326 | 11.0 | 110 | 0.6740 | {'precision': 0.7254464285714286, 'recall': 0.8034610630407911, 'f1': 0.7624633431085045, 'number': 809} | {'precision': 0.3356643356643357, 'recall': 0.40336134453781514, 'f1': 0.366412213740458, 'number': 119} | {'precision': 0.7606614447345518, 'recall': 0.8206572769953052, 'f1': 0.7895212285456188, 'number': 1065} | 0.7185 | 0.7888 | 0.7520 | 0.8130 |
| 0.307 | 12.0 | 120 | 0.6741 | {'precision': 0.7319004524886877, 'recall': 0.799752781211372, 'f1': 0.7643236857649144, 'number': 809} | {'precision': 0.3548387096774194, 'recall': 0.3697478991596639, 'f1': 0.36213991769547327, 'number': 119} | {'precision': 0.7879325643300799, 'recall': 0.8338028169014085, 'f1': 0.8102189781021898, 'number': 1065} | 0.7396 | 0.7923 | 0.7650 | 0.8132 |
| 0.2926 | 13.0 | 130 | 0.6789 | {'precision': 0.7275784753363229, 'recall': 0.8022249690976514, 'f1': 0.7630805408583187, 'number': 809} | {'precision': 0.3409090909090909, 'recall': 0.37815126050420167, 'f1': 0.3585657370517928, 'number': 119} | {'precision': 0.7806167400881058, 'recall': 0.831924882629108, 'f1': 0.8054545454545454, 'number': 1065} | 0.7318 | 0.7928 | 0.7611 | 0.8147 |
| 0.278 | 14.0 | 140 | 0.6796 | {'precision': 0.723404255319149, 'recall': 0.7985166872682324, 'f1': 0.7591069330199766, 'number': 809} | {'precision': 0.35384615384615387, 'recall': 0.3865546218487395, 'f1': 0.3694779116465864, 'number': 119} | {'precision': 0.7834507042253521, 'recall': 0.8356807511737089, 'f1': 0.8087233075874602, 'number': 1065} | 0.7327 | 0.7938 | 0.7620 | 0.8170 |
| 0.2745 | 15.0 | 150 | 0.6826 | {'precision': 0.7305524239007892, 'recall': 0.8009888751545118, 'f1': 0.7641509433962265, 'number': 809} | {'precision': 0.3643410852713178, 'recall': 0.3949579831932773, 'f1': 0.3790322580645162, 'number': 119} | {'precision': 0.7896613190730838, 'recall': 0.831924882629108, 'f1': 0.8102423411065386, 'number': 1065} | 0.7395 | 0.7933 | 0.7654 | 0.8177 |
### Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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