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library_name: transformers
license: mit
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: 0.6864
- Answer: {'precision': 0.6979722518676628, 'recall': 0.8084054388133498, 'f1': 0.7491408934707904, 'number': 809}
- Header: {'precision': 0.296875, 'recall': 0.31932773109243695, 'f1': 0.3076923076923077, 'number': 119}
- Question: {'precision': 0.7652790079716564, 'recall': 0.8112676056338028, 'f1': 0.7876025524156791, 'number': 1065}
- Overall Precision: 0.7092
- Overall Recall: 0.7807
- Overall F1: 0.7433
- Overall Accuracy: 0.8094
## 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 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7694 | 1.0 | 10 | 1.6060 | {'precision': 0.024282560706401765, 'recall': 0.013597033374536464, 'f1': 0.01743264659270998, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3227176220806794, 'recall': 0.14272300469483568, 'f1': 0.19791666666666666, 'number': 1065} | 0.1764 | 0.0818 | 0.1118 | 0.3371 |
| 1.456 | 2.0 | 20 | 1.2789 | {'precision': 0.21739130434782608, 'recall': 0.315203955500618, 'f1': 0.2573158425832492, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.37261146496815284, 'recall': 0.5492957746478874, 'f1': 0.444022770398482, 'number': 1065} | 0.3062 | 0.4215 | 0.3547 | 0.5669 |
| 1.1265 | 3.0 | 30 | 0.9633 | {'precision': 0.46710526315789475, 'recall': 0.6143386897404203, 'f1': 0.5306994127068873, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5227606461086637, 'recall': 0.6685446009389672, 'f1': 0.5867325916769675, 'number': 1065} | 0.4984 | 0.6066 | 0.5472 | 0.6822 |
| 0.8681 | 4.0 | 40 | 0.8085 | {'precision': 0.5848690591658584, 'recall': 0.7453646477132262, 'f1': 0.6554347826086957, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.6259607173356105, 'recall': 0.6882629107981221, 'f1': 0.6556350626118067, 'number': 1065} | 0.6026 | 0.6703 | 0.6347 | 0.7390 |
| 0.6998 | 5.0 | 50 | 0.7327 | {'precision': 0.61875, 'recall': 0.7342398022249691, 'f1': 0.6715658564160543, 'number': 809} | {'precision': 0.14285714285714285, 'recall': 0.07563025210084033, 'f1': 0.0989010989010989, 'number': 119} | {'precision': 0.6388676358071921, 'recall': 0.784037558685446, 'f1': 0.7040472175379427, 'number': 1065} | 0.6172 | 0.7215 | 0.6653 | 0.7721 |
| 0.5889 | 6.0 | 60 | 0.6932 | {'precision': 0.6111111111111112, 'recall': 0.788627935723115, 'f1': 0.6886130599028603, 'number': 809} | {'precision': 0.1791044776119403, 'recall': 0.10084033613445378, 'f1': 0.12903225806451613, 'number': 119} | {'precision': 0.7079964061096137, 'recall': 0.739906103286385, 'f1': 0.7235996326905417, 'number': 1065} | 0.6466 | 0.7215 | 0.6820 | 0.7731 |
| 0.5103 | 7.0 | 70 | 0.6603 | {'precision': 0.6570247933884298, 'recall': 0.7861557478368356, 'f1': 0.7158131682611143, 'number': 809} | {'precision': 0.3058823529411765, 'recall': 0.2184873949579832, 'f1': 0.2549019607843137, 'number': 119} | {'precision': 0.7271937445699392, 'recall': 0.7859154929577464, 'f1': 0.7554151624548736, 'number': 1065} | 0.6801 | 0.7521 | 0.7143 | 0.7886 |
| 0.4557 | 8.0 | 80 | 0.6577 | {'precision': 0.649949849548646, 'recall': 0.8009888751545118, 'f1': 0.7176079734219271, 'number': 809} | {'precision': 0.2641509433962264, 'recall': 0.23529411764705882, 'f1': 0.24888888888888888, 'number': 119} | {'precision': 0.7243150684931506, 'recall': 0.7943661971830986, 'f1': 0.7577250335871025, 'number': 1065} | 0.6702 | 0.7637 | 0.7139 | 0.7959 |
| 0.3927 | 9.0 | 90 | 0.6559 | {'precision': 0.6729559748427673, 'recall': 0.7935723114956736, 'f1': 0.7283040272263188, 'number': 809} | {'precision': 0.29310344827586204, 'recall': 0.2857142857142857, 'f1': 0.2893617021276596, 'number': 119} | {'precision': 0.7451838879159369, 'recall': 0.7990610328638498, 'f1': 0.7711826008155868, 'number': 1065} | 0.6903 | 0.7662 | 0.7263 | 0.8041 |
| 0.3806 | 10.0 | 100 | 0.6697 | {'precision': 0.6778242677824268, 'recall': 0.8009888751545118, 'f1': 0.7342776203966006, 'number': 809} | {'precision': 0.2719298245614035, 'recall': 0.2605042016806723, 'f1': 0.26609442060085836, 'number': 119} | {'precision': 0.7642418930762489, 'recall': 0.8187793427230047, 'f1': 0.7905711695376247, 'number': 1065} | 0.7015 | 0.7782 | 0.7379 | 0.8083 |
| 0.3299 | 11.0 | 110 | 0.6691 | {'precision': 0.6905016008537886, 'recall': 0.799752781211372, 'f1': 0.7411225658648338, 'number': 809} | {'precision': 0.30578512396694213, 'recall': 0.31092436974789917, 'f1': 0.30833333333333335, 'number': 119} | {'precision': 0.7675628794449263, 'recall': 0.8309859154929577, 'f1': 0.7980162308385933, 'number': 1065} | 0.7096 | 0.7873 | 0.7464 | 0.8102 |
| 0.3093 | 12.0 | 120 | 0.6782 | {'precision': 0.6955128205128205, 'recall': 0.8046971569839307, 'f1': 0.746131805157593, 'number': 809} | {'precision': 0.3008130081300813, 'recall': 0.31092436974789917, 'f1': 0.3057851239669422, 'number': 119} | {'precision': 0.7582222222222222, 'recall': 0.8009389671361502, 'f1': 0.7789954337899543, 'number': 1065} | 0.7056 | 0.7732 | 0.7379 | 0.8096 |
| 0.2923 | 13.0 | 130 | 0.6818 | {'precision': 0.6923890063424947, 'recall': 0.8096415327564895, 'f1': 0.7464387464387465, 'number': 809} | {'precision': 0.3217391304347826, 'recall': 0.31092436974789917, 'f1': 0.3162393162393162, 'number': 119} | {'precision': 0.7752212389380531, 'recall': 0.8225352112676056, 'f1': 0.7981776765375853, 'number': 1065} | 0.7157 | 0.7868 | 0.7495 | 0.8091 |
| 0.2694 | 14.0 | 140 | 0.6849 | {'precision': 0.7018299246501615, 'recall': 0.8059332509270705, 'f1': 0.7502876869965477, 'number': 809} | {'precision': 0.29133858267716534, 'recall': 0.31092436974789917, 'f1': 0.3008130081300813, 'number': 119} | {'precision': 0.7716814159292036, 'recall': 0.8187793427230047, 'f1': 0.7945330296127562, 'number': 1065} | 0.7141 | 0.7832 | 0.7471 | 0.8097 |
| 0.2738 | 15.0 | 150 | 0.6864 | {'precision': 0.6979722518676628, 'recall': 0.8084054388133498, 'f1': 0.7491408934707904, 'number': 809} | {'precision': 0.296875, 'recall': 0.31932773109243695, 'f1': 0.3076923076923077, 'number': 119} | {'precision': 0.7652790079716564, 'recall': 0.8112676056338028, 'f1': 0.7876025524156791, 'number': 1065} | 0.7092 | 0.7807 | 0.7433 | 0.8094 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
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