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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: 0.7008
- Answer: {'precision': 0.7050592034445641, 'recall': 0.8096415327564895, 'f1': 0.7537399309551209, 'number': 809}
- Header: {'precision': 0.2803030303030303, 'recall': 0.31092436974789917, 'f1': 0.29482071713147406, 'number': 119}
- Question: {'precision': 0.7809187279151943, 'recall': 0.8300469483568075, 'f1': 0.8047337278106509, 'number': 1065}
- Overall Precision: 0.7187
- Overall Recall: 0.7908
- Overall F1: 0.7530
- Overall Accuracy: 0.8087
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.787 | 1.0 | 10 | 1.5982 | {'precision': 0.02607561929595828, 'recall': 0.024721878862793572, 'f1': 0.025380710659898473, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2468354430379747, 'recall': 0.21971830985915494, 'f1': 0.23248882265275708, 'number': 1065} | 0.1481 | 0.1274 | 0.1370 | 0.3555 |
| 1.4393 | 2.0 | 20 | 1.2504 | {'precision': 0.10978520286396182, 'recall': 0.11372064276885044, 'f1': 0.1117182756527019, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4381169324221716, 'recall': 0.5417840375586854, 'f1': 0.4844668345927792, 'number': 1065} | 0.3104 | 0.3357 | 0.3226 | 0.5539 |
| 1.0904 | 3.0 | 30 | 0.9333 | {'precision': 0.5273109243697479, 'recall': 0.6205191594561187, 'f1': 0.5701306076093129, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5661929693343306, 'recall': 0.7107981220657277, 'f1': 0.630308076602831, 'number': 1065} | 0.5436 | 0.6317 | 0.5844 | 0.7201 |
| 0.8353 | 4.0 | 40 | 0.7609 | {'precision': 0.6157786885245902, 'recall': 0.7428924598269468, 'f1': 0.673389355742297, 'number': 809} | {'precision': 0.10344827586206896, 'recall': 0.05042016806722689, 'f1': 0.06779661016949153, 'number': 119} | {'precision': 0.651414309484193, 'recall': 0.7352112676056338, 'f1': 0.6907807675341862, 'number': 1065} | 0.6216 | 0.6974 | 0.6574 | 0.7679 |
| 0.6619 | 5.0 | 50 | 0.7136 | {'precision': 0.6655879180151025, 'recall': 0.7626699629171817, 'f1': 0.7108294930875575, 'number': 809} | {'precision': 0.275, 'recall': 0.18487394957983194, 'f1': 0.22110552763819097, 'number': 119} | {'precision': 0.685214626391097, 'recall': 0.8093896713615023, 'f1': 0.7421437795953508, 'number': 1065} | 0.6627 | 0.7531 | 0.7050 | 0.7866 |
| 0.5642 | 6.0 | 60 | 0.6861 | {'precision': 0.6413373860182371, 'recall': 0.7824474660074165, 'f1': 0.7048997772828508, 'number': 809} | {'precision': 0.3382352941176471, 'recall': 0.19327731092436976, 'f1': 0.24598930481283424, 'number': 119} | {'precision': 0.7156357388316151, 'recall': 0.7821596244131456, 'f1': 0.7474203678779722, 'number': 1065} | 0.6710 | 0.7471 | 0.7070 | 0.7846 |
| 0.4894 | 7.0 | 70 | 0.6645 | {'precision': 0.6925601750547046, 'recall': 0.7824474660074165, 'f1': 0.73476494486361, 'number': 809} | {'precision': 0.3106796116504854, 'recall': 0.2689075630252101, 'f1': 0.28828828828828823, 'number': 119} | {'precision': 0.7319762510602206, 'recall': 0.8103286384976526, 'f1': 0.7691622103386809, 'number': 1065} | 0.6958 | 0.7667 | 0.7295 | 0.7993 |
| 0.4396 | 8.0 | 80 | 0.6633 | {'precision': 0.68580375782881, 'recall': 0.8121137206427689, 'f1': 0.7436332767402377, 'number': 809} | {'precision': 0.25210084033613445, 'recall': 0.25210084033613445, 'f1': 0.25210084033613445, 'number': 119} | {'precision': 0.7321131447587355, 'recall': 0.8262910798122066, 'f1': 0.776356418173798, 'number': 1065} | 0.6876 | 0.7863 | 0.7336 | 0.8033 |
| 0.381 | 9.0 | 90 | 0.6612 | {'precision': 0.7039473684210527, 'recall': 0.7935723114956736, 'f1': 0.7460778617083091, 'number': 809} | {'precision': 0.2920353982300885, 'recall': 0.2773109243697479, 'f1': 0.28448275862068967, 'number': 119} | {'precision': 0.7660869565217391, 'recall': 0.8272300469483568, 'f1': 0.7954853273137698, 'number': 1065} | 0.7154 | 0.7807 | 0.7466 | 0.8040 |
| 0.3737 | 10.0 | 100 | 0.6725 | {'precision': 0.6994652406417112, 'recall': 0.8084054388133498, 'f1': 0.7499999999999999, 'number': 809} | {'precision': 0.2818181818181818, 'recall': 0.2605042016806723, 'f1': 0.27074235807860264, 'number': 119} | {'precision': 0.7605512489233419, 'recall': 0.8291079812206573, 'f1': 0.7933513027852651, 'number': 1065} | 0.7108 | 0.7868 | 0.7468 | 0.8067 |
| 0.3174 | 11.0 | 110 | 0.6862 | {'precision': 0.7039827771797632, 'recall': 0.8084054388133498, 'f1': 0.7525891829689298, 'number': 809} | {'precision': 0.2713178294573643, 'recall': 0.29411764705882354, 'f1': 0.28225806451612906, 'number': 119} | {'precision': 0.7706342311033884, 'recall': 0.8328638497652582, 'f1': 0.8005415162454873, 'number': 1065} | 0.7134 | 0.7908 | 0.7501 | 0.8033 |
| 0.2976 | 12.0 | 120 | 0.6907 | {'precision': 0.7048648648648649, 'recall': 0.8059332509270705, 'f1': 0.7520184544405998, 'number': 809} | {'precision': 0.2926829268292683, 'recall': 0.3025210084033613, 'f1': 0.2975206611570248, 'number': 119} | {'precision': 0.7772887323943662, 'recall': 0.8291079812206573, 'f1': 0.8023625624716039, 'number': 1065} | 0.7193 | 0.7883 | 0.7522 | 0.8081 |
| 0.2799 | 13.0 | 130 | 0.6973 | {'precision': 0.7105549510337323, 'recall': 0.8071693448702101, 'f1': 0.755787037037037, 'number': 809} | {'precision': 0.31451612903225806, 'recall': 0.3277310924369748, 'f1': 0.32098765432098764, 'number': 119} | {'precision': 0.7857777777777778, 'recall': 0.8300469483568075, 'f1': 0.8073059360730593, 'number': 1065} | 0.7269 | 0.7908 | 0.7575 | 0.8066 |
| 0.2597 | 14.0 | 140 | 0.7004 | {'precision': 0.7083786724700761, 'recall': 0.8046971569839307, 'f1': 0.7534722222222221, 'number': 809} | {'precision': 0.2803030303030303, 'recall': 0.31092436974789917, 'f1': 0.29482071713147406, 'number': 119} | {'precision': 0.781195079086116, 'recall': 0.8347417840375587, 'f1': 0.8070812528370404, 'number': 1065} | 0.7204 | 0.7913 | 0.7542 | 0.8073 |
| 0.2627 | 15.0 | 150 | 0.7008 | {'precision': 0.7050592034445641, 'recall': 0.8096415327564895, 'f1': 0.7537399309551209, 'number': 809} | {'precision': 0.2803030303030303, 'recall': 0.31092436974789917, 'f1': 0.29482071713147406, 'number': 119} | {'precision': 0.7809187279151943, 'recall': 0.8300469483568075, 'f1': 0.8047337278106509, 'number': 1065} | 0.7187 | 0.7908 | 0.7530 | 0.8087 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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