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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: 0.7047
- Answer: {'precision': 0.7122222222222222, 'recall': 0.792336217552534, 'f1': 0.7501462843768285, 'number': 809}
- Header: {'precision': 0.325, 'recall': 0.3277310924369748, 'f1': 0.3263598326359833, 'number': 119}
- Question: {'precision': 0.7794508414526129, 'recall': 0.8262910798122066, 'f1': 0.8021877848678213, 'number': 1065}
- Overall Precision: 0.7259
- Overall Recall: 0.7827
- Overall F1: 0.7533
- Overall Accuracy: 0.8068
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8367 | 1.0 | 10 | 1.6199 | {'precision': 0.017991004497751123, 'recall': 0.014833127317676144, 'f1': 0.016260162601626018, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.21987951807228914, 'recall': 0.13708920187793427, 'f1': 0.16888374783111626, 'number': 1065} | 0.1187 | 0.0793 | 0.0951 | 0.3426 |
| 1.4807 | 2.0 | 20 | 1.2596 | {'precision': 0.18181818181818182, 'recall': 0.20519159456118666, 'f1': 0.1927990708478513, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.46142208774583965, 'recall': 0.5727699530516432, 'f1': 0.5111018014243821, 'number': 1065} | 0.3472 | 0.3894 | 0.3671 | 0.5904 |
| 1.1183 | 3.0 | 30 | 0.9406 | {'precision': 0.4608967674661105, 'recall': 0.546353522867738, 'f1': 0.5, 'number': 809} | {'precision': 0.03225806451612903, 'recall': 0.008403361344537815, 'f1': 0.013333333333333332, 'number': 119} | {'precision': 0.56973293768546, 'recall': 0.7211267605633803, 'f1': 0.6365520099461252, 'number': 1065} | 0.5180 | 0.6076 | 0.5592 | 0.7099 |
| 0.8592 | 4.0 | 40 | 0.7881 | {'precision': 0.5798237022526934, 'recall': 0.7317676143386898, 'f1': 0.6469945355191257, 'number': 809} | {'precision': 0.10714285714285714, 'recall': 0.05042016806722689, 'f1': 0.06857142857142856, 'number': 119} | {'precision': 0.6663865546218487, 'recall': 0.7446009389671362, 'f1': 0.7033259423503326, 'number': 1065} | 0.6136 | 0.6979 | 0.6531 | 0.7604 |
| 0.6864 | 5.0 | 50 | 0.7305 | {'precision': 0.6115261472785486, 'recall': 0.7082818294190358, 'f1': 0.6563573883161512, 'number': 809} | {'precision': 0.23529411764705882, 'recall': 0.16806722689075632, 'f1': 0.19607843137254902, 'number': 119} | {'precision': 0.6808510638297872, 'recall': 0.8112676056338028, 'f1': 0.7403598971722366, 'number': 1065} | 0.6360 | 0.7311 | 0.6802 | 0.7749 |
| 0.584 | 6.0 | 60 | 0.6955 | {'precision': 0.6358024691358025, 'recall': 0.7639060568603214, 'f1': 0.6939921392476137, 'number': 809} | {'precision': 0.28205128205128205, 'recall': 0.18487394957983194, 'f1': 0.2233502538071066, 'number': 119} | {'precision': 0.7248495270851246, 'recall': 0.7915492957746478, 'f1': 0.7567324955116697, 'number': 1065} | 0.6701 | 0.7441 | 0.7052 | 0.7810 |
| 0.5083 | 7.0 | 70 | 0.6726 | {'precision': 0.6795698924731183, 'recall': 0.7812113720642769, 'f1': 0.7268545140885566, 'number': 809} | {'precision': 0.27, 'recall': 0.226890756302521, 'f1': 0.24657534246575347, 'number': 119} | {'precision': 0.7439236111111112, 'recall': 0.8046948356807512, 'f1': 0.7731168245376635, 'number': 1065} | 0.6948 | 0.7607 | 0.7262 | 0.7933 |
| 0.4552 | 8.0 | 80 | 0.6811 | {'precision': 0.6750788643533123, 'recall': 0.7935723114956736, 'f1': 0.7295454545454545, 'number': 809} | {'precision': 0.23214285714285715, 'recall': 0.2184873949579832, 'f1': 0.22510822510822512, 'number': 119} | {'precision': 0.7482817869415808, 'recall': 0.8178403755868544, 'f1': 0.781516375056079, 'number': 1065} | 0.6911 | 0.7722 | 0.7294 | 0.7959 |
| 0.4053 | 9.0 | 90 | 0.6773 | {'precision': 0.7058177826564215, 'recall': 0.7948084054388134, 'f1': 0.7476744186046511, 'number': 809} | {'precision': 0.27450980392156865, 'recall': 0.23529411764705882, 'f1': 0.2533936651583711, 'number': 119} | {'precision': 0.7614840989399293, 'recall': 0.8093896713615023, 'f1': 0.7847064178425124, 'number': 1065} | 0.7147 | 0.7692 | 0.7409 | 0.7999 |
| 0.3938 | 10.0 | 100 | 0.6783 | {'precision': 0.6976998904709748, 'recall': 0.7873918417799752, 'f1': 0.7398373983739838, 'number': 809} | {'precision': 0.2894736842105263, 'recall': 0.2773109243697479, 'f1': 0.2832618025751073, 'number': 119} | {'precision': 0.7643979057591623, 'recall': 0.8225352112676056, 'f1': 0.7924016282225238, 'number': 1065} | 0.7115 | 0.7757 | 0.7422 | 0.8007 |
| 0.3377 | 11.0 | 110 | 0.6881 | {'precision': 0.7136465324384788, 'recall': 0.788627935723115, 'f1': 0.7492660011743981, 'number': 809} | {'precision': 0.3103448275862069, 'recall': 0.3025210084033613, 'f1': 0.30638297872340425, 'number': 119} | {'precision': 0.7664359861591695, 'recall': 0.831924882629108, 'f1': 0.7978388113462405, 'number': 1065} | 0.7202 | 0.7827 | 0.7502 | 0.8033 |
| 0.3211 | 12.0 | 120 | 0.6958 | {'precision': 0.7075575027382256, 'recall': 0.7985166872682324, 'f1': 0.7502903600464577, 'number': 809} | {'precision': 0.31092436974789917, 'recall': 0.31092436974789917, 'f1': 0.31092436974789917, 'number': 119} | {'precision': 0.7762923351158645, 'recall': 0.8178403755868544, 'f1': 0.79652491998171, 'number': 1065} | 0.7214 | 0.7797 | 0.7495 | 0.8048 |
| 0.3036 | 13.0 | 130 | 0.7008 | {'precision': 0.7138121546961326, 'recall': 0.7985166872682324, 'f1': 0.7537922987164527, 'number': 809} | {'precision': 0.32727272727272727, 'recall': 0.3025210084033613, 'f1': 0.314410480349345, 'number': 119} | {'precision': 0.7775800711743772, 'recall': 0.8206572769953052, 'f1': 0.7985381452718137, 'number': 1065} | 0.7274 | 0.7807 | 0.7531 | 0.8049 |
| 0.2798 | 14.0 | 140 | 0.7025 | {'precision': 0.7131696428571429, 'recall': 0.7898640296662547, 'f1': 0.7495601173020529, 'number': 809} | {'precision': 0.3274336283185841, 'recall': 0.31092436974789917, 'f1': 0.3189655172413793, 'number': 119} | {'precision': 0.7727272727272727, 'recall': 0.8300469483568075, 'f1': 0.8003621548211861, 'number': 1065} | 0.7246 | 0.7827 | 0.7525 | 0.8066 |
| 0.279 | 15.0 | 150 | 0.7047 | {'precision': 0.7122222222222222, 'recall': 0.792336217552534, 'f1': 0.7501462843768285, 'number': 809} | {'precision': 0.325, 'recall': 0.3277310924369748, 'f1': 0.3263598326359833, 'number': 119} | {'precision': 0.7794508414526129, 'recall': 0.8262910798122066, 'f1': 0.8021877848678213, 'number': 1065} | 0.7259 | 0.7827 | 0.7533 | 0.8068 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
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