---
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layoutlm-funsd3
  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-funsd3

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: 1.1833
- Answer: {'precision': 0.2526041666666667, 'recall': 0.23980222496909764, 'f1': 0.24603677869372226, 'number': 809}
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
- Question: {'precision': 0.4935064935064935, 'recall': 0.5352112676056338, 'f1': 0.5135135135135136, 'number': 1065}
- Overall Precision: 0.3973
- Overall Recall: 0.3833
- Overall F1: 0.3902
- Overall Accuracy: 0.6048

## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 12
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                         | Header                                                                                                         | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.9597        | 1.0   | 10   | 1.9692          | {'precision': 0.024831867563372995, 'recall': 0.059332509270704575, 'f1': 0.0350109409190372, 'number': 809}   | {'precision': 0.0054894784995425435, 'recall': 0.05042016806722689, 'f1': 0.009900990099009901, 'number': 119} | {'precision': 0.05862516212710765, 'recall': 0.21220657276995306, 'f1': 0.091869918699187, 'number': 1065}   | 0.0407            | 0.1405         | 0.0631     | 0.1655           |
| 1.9429        | 2.0   | 20   | 1.9517          | {'precision': 0.02355889724310777, 'recall': 0.0580964153275649, 'f1': 0.033523537803138374, 'number': 809}    | {'precision': 0.006984866123399301, 'recall': 0.05042016806722689, 'f1': 0.012269938650306747, 'number': 119}  | {'precision': 0.06357435197817189, 'recall': 0.2187793427230047, 'f1': 0.09852008456659618, 'number': 1065}  | 0.0439            | 0.1435         | 0.0672     | 0.1837           |
| 1.9283        | 3.0   | 30   | 1.9222          | {'precision': 0.02506265664160401, 'recall': 0.06180469715698393, 'f1': 0.035663338088445073, 'number': 809}   | {'precision': 0.005802707930367505, 'recall': 0.025210084033613446, 'f1': 0.009433962264150943, 'number': 119} | {'precision': 0.0683998761993191, 'recall': 0.20751173708920187, 'f1': 0.10288640595903166, 'number': 1065}  | 0.0477            | 0.1375         | 0.0708     | 0.2076           |
| 1.8979        | 4.0   | 40   | 1.8822          | {'precision': 0.026082130965593784, 'recall': 0.0580964153275649, 'f1': 0.03600153198008425, 'number': 809}    | {'precision': 0.01327433628318584, 'recall': 0.025210084033613446, 'f1': 0.017391304347826087, 'number': 119}  | {'precision': 0.07303807303807304, 'recall': 0.17652582159624414, 'f1': 0.10332508931025007, 'number': 1065} | 0.0517            | 0.1194         | 0.0722     | 0.2412           |
| 1.8452        | 5.0   | 50   | 1.8330          | {'precision': 0.02404809619238477, 'recall': 0.04449938195302843, 'f1': 0.03122289679098005, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.08863636363636364, 'recall': 0.14647887323943662, 'f1': 0.11044247787610618, 'number': 1065} | 0.0577            | 0.0963         | 0.0722     | 0.2719           |
| 1.7986        | 6.0   | 60   | 1.7759          | {'precision': 0.017241379310344827, 'recall': 0.022249690976514216, 'f1': 0.019427954668105776, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.1244196843082637, 'recall': 0.12582159624413145, 'f1': 0.12511671335200747, 'number': 1065}  | 0.0713            | 0.0763         | 0.0737     | 0.3012           |
| 1.7397        | 7.0   | 70   | 1.7107          | {'precision': 0.02045728038507822, 'recall': 0.021013597033374538, 'f1': 0.02073170731707317, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.18026315789473685, 'recall': 0.12863849765258217, 'f1': 0.15013698630136987, 'number': 1065} | 0.0967            | 0.0773         | 0.0859     | 0.3270           |
| 1.6707        | 8.0   | 80   | 1.6298          | {'precision': 0.03066271018793274, 'recall': 0.038318912237330034, 'f1': 0.03406593406593406, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.20087815587266739, 'recall': 0.17183098591549295, 'f1': 0.18522267206477733, 'number': 1065} | 0.1113            | 0.1074         | 0.1093     | 0.3654           |
| 1.5891        | 9.0   | 90   | 1.5416          | {'precision': 0.047619047619047616, 'recall': 0.06674907292954264, 'f1': 0.055584148224395266, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.2087421944692239, 'recall': 0.21971830985915494, 'f1': 0.2140896614821592, 'number': 1065}   | 0.1277            | 0.1445         | 0.1356     | 0.4183           |
| 1.516         | 10.0  | 100  | 1.4443          | {'precision': 0.06370070778564206, 'recall': 0.07787391841779975, 'f1': 0.07007786429365963, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.2838983050847458, 'recall': 0.3145539906103286, 'f1': 0.2984409799554566, 'number': 1065}    | 0.1835            | 0.1997         | 0.1913     | 0.4720           |
| 1.3887        | 11.0  | 110  | 1.3259          | {'precision': 0.11662531017369727, 'recall': 0.1161928306551298, 'f1': 0.11640866873065016, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.39842381786339753, 'recall': 0.4272300469483568, 'f1': 0.412324422292705, 'number': 1065}    | 0.2818            | 0.2755         | 0.2786     | 0.5434           |
| 1.261         | 12.0  | 120  | 1.1833          | {'precision': 0.2526041666666667, 'recall': 0.23980222496909764, 'f1': 0.24603677869372226, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.4935064935064935, 'recall': 0.5352112676056338, 'f1': 0.5135135135135136, 'number': 1065}    | 0.3973            | 0.3833         | 0.3902     | 0.6048           |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1