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# 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: 0.7207
- Answer: {'precision': 0.7114754098360656, 'recall': 0.8046971569839307, 'f1': 0.7552204176334106, 'number': 809}
- Header: {'precision': 0.3025210084033613, 'recall': 0.3025210084033613, 'f1': 0.3025210084033613, 'number': 119}
- Question: {'precision': 0.7707061900610288, 'recall': 0.8300469483568075, 'f1': 0.7992766726943942, 'number': 1065}
- Overall Precision: 0.7203
- Overall Recall: 0.7883
- Overall F1: 0.7528
- Overall Accuracy: 0.7971

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8417        | 1.0   | 10   | 1.6166          | {'precision': 0.028741328047571853, 'recall': 0.03584672435105068, 'f1': 0.0319031903190319, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.16827852998065765, 'recall': 0.16338028169014085, 'f1': 0.16579323487374942, 'number': 1065} | 0.0993            | 0.1019         | 0.1006     | 0.3810           |
| 1.4476        | 2.0   | 20   | 1.2599          | {'precision': 0.15711947626841244, 'recall': 0.11866501854140915, 'f1': 0.13521126760563382, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.46172441579371476, 'recall': 0.5380281690140845, 'f1': 0.4969644405897658, 'number': 1065}   | 0.3612            | 0.3357         | 0.3480     | 0.5747           |
| 1.1227        | 3.0   | 30   | 0.9780          | {'precision': 0.4661214953271028, 'recall': 0.4932014833127318, 'f1': 0.4792792792792793, 'number': 809}    | {'precision': 0.16279069767441862, 'recall': 0.058823529411764705, 'f1': 0.08641975308641975, 'number': 119} | {'precision': 0.5979020979020979, 'recall': 0.6422535211267606, 'f1': 0.6192847442281576, 'number': 1065}    | 0.5335            | 0.5469         | 0.5401     | 0.7036           |
| 0.8596        | 4.0   | 40   | 0.7946          | {'precision': 0.5789473684210527, 'recall': 0.6526576019777504, 'f1': 0.6135967460778617, 'number': 809}    | {'precision': 0.24193548387096775, 'recall': 0.12605042016806722, 'f1': 0.16574585635359115, 'number': 119}  | {'precision': 0.6658141517476556, 'recall': 0.7333333333333333, 'f1': 0.6979445933869526, 'number': 1065}    | 0.6167            | 0.6643         | 0.6396     | 0.7589           |
| 0.6705        | 5.0   | 50   | 0.7132          | {'precision': 0.6424759871931697, 'recall': 0.7441285537700866, 'f1': 0.6895761741122567, 'number': 809}    | {'precision': 0.3333333333333333, 'recall': 0.21008403361344538, 'f1': 0.2577319587628866, 'number': 119}    | {'precision': 0.6747376916868443, 'recall': 0.7849765258215963, 'f1': 0.7256944444444444, 'number': 1065}    | 0.6499            | 0.7341         | 0.6894     | 0.7767           |
| 0.5653        | 6.0   | 60   | 0.6840          | {'precision': 0.653125, 'recall': 0.7750309023485785, 'f1': 0.7088750706613907, 'number': 809}              | {'precision': 0.30952380952380953, 'recall': 0.2184873949579832, 'f1': 0.2561576354679803, 'number': 119}    | {'precision': 0.7077814569536424, 'recall': 0.8028169014084507, 'f1': 0.7523097228332599, 'number': 1065}    | 0.6696            | 0.7566         | 0.7105     | 0.7846           |
| 0.4959        | 7.0   | 70   | 0.6684          | {'precision': 0.6872964169381107, 'recall': 0.7824474660074165, 'f1': 0.7317919075144509, 'number': 809}    | {'precision': 0.2815533980582524, 'recall': 0.24369747899159663, 'f1': 0.26126126126126126, 'number': 119}   | {'precision': 0.734006734006734, 'recall': 0.8187793427230047, 'f1': 0.7740790057700843, 'number': 1065}     | 0.6935            | 0.7697         | 0.7296     | 0.7950           |
| 0.4343        | 8.0   | 80   | 0.6696          | {'precision': 0.6898395721925134, 'recall': 0.7972805933250927, 'f1': 0.7396788990825688, 'number': 809}    | {'precision': 0.26956521739130435, 'recall': 0.2605042016806723, 'f1': 0.264957264957265, 'number': 119}     | {'precision': 0.7495769881556683, 'recall': 0.831924882629108, 'f1': 0.7886070315976857, 'number': 1065}     | 0.6998            | 0.7837         | 0.7394     | 0.7987           |
| 0.375         | 9.0   | 90   | 0.6760          | {'precision': 0.7105549510337323, 'recall': 0.8071693448702101, 'f1': 0.755787037037037, 'number': 809}     | {'precision': 0.25, 'recall': 0.2605042016806723, 'f1': 0.25514403292181076, 'number': 119}                  | {'precision': 0.7758007117437722, 'recall': 0.8187793427230047, 'f1': 0.7967108268615805, 'number': 1065}    | 0.7180            | 0.7807         | 0.7481     | 0.7992           |
| 0.3532        | 10.0  | 100  | 0.6802          | {'precision': 0.7147470398277718, 'recall': 0.8207663782447466, 'f1': 0.7640966628308401, 'number': 809}    | {'precision': 0.3018867924528302, 'recall': 0.2689075630252101, 'f1': 0.28444444444444444, 'number': 119}    | {'precision': 0.7761061946902655, 'recall': 0.8234741784037559, 'f1': 0.7990888382687927, 'number': 1065}    | 0.7266            | 0.7893         | 0.7566     | 0.8046           |
| 0.3265        | 11.0  | 110  | 0.6995          | {'precision': 0.6968716289104638, 'recall': 0.7985166872682324, 'f1': 0.7442396313364056, 'number': 809}    | {'precision': 0.308411214953271, 'recall': 0.2773109243697479, 'f1': 0.29203539823008845, 'number': 119}     | {'precision': 0.7589134125636672, 'recall': 0.8394366197183099, 'f1': 0.7971466785555059, 'number': 1065}    | 0.7111            | 0.7893         | 0.7482     | 0.7973           |
| 0.3023        | 12.0  | 120  | 0.7053          | {'precision': 0.7027896995708155, 'recall': 0.8096415327564895, 'f1': 0.7524411257897761, 'number': 809}    | {'precision': 0.30303030303030304, 'recall': 0.33613445378151263, 'f1': 0.3187250996015936, 'number': 119}   | {'precision': 0.769434628975265, 'recall': 0.8178403755868544, 'f1': 0.7928994082840236, 'number': 1065}     | 0.7131            | 0.7858         | 0.7477     | 0.7991           |
| 0.2927        | 13.0  | 130  | 0.7080          | {'precision': 0.7024704618689581, 'recall': 0.8084054388133498, 'f1': 0.7517241379310345, 'number': 809}    | {'precision': 0.3125, 'recall': 0.29411764705882354, 'f1': 0.30303030303030304, 'number': 119}               | {'precision': 0.7679033649698016, 'recall': 0.8356807511737089, 'f1': 0.8003597122302158, 'number': 1065}    | 0.7171            | 0.7923         | 0.7528     | 0.7999           |
| 0.2756        | 14.0  | 140  | 0.7128          | {'precision': 0.7081967213114754, 'recall': 0.8009888751545118, 'f1': 0.7517401392111368, 'number': 809}    | {'precision': 0.32142857142857145, 'recall': 0.3025210084033613, 'f1': 0.3116883116883117, 'number': 119}    | {'precision': 0.7720524017467248, 'recall': 0.8300469483568075, 'f1': 0.7999999999999999, 'number': 1065}    | 0.7219            | 0.7868         | 0.7529     | 0.7984           |
| 0.2741        | 15.0  | 150  | 0.7207          | {'precision': 0.7114754098360656, 'recall': 0.8046971569839307, 'f1': 0.7552204176334106, 'number': 809}    | {'precision': 0.3025210084033613, 'recall': 0.3025210084033613, 'f1': 0.3025210084033613, 'number': 119}     | {'precision': 0.7707061900610288, 'recall': 0.8300469483568075, 'f1': 0.7992766726943942, 'number': 1065}    | 0.7203            | 0.7883         | 0.7528     | 0.7971           |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0

{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/layoutlm-base-uncased", "model-index": [{"name": "layoutlm-funsd", "results": []}]}
transformers tensorboard safetensors layoutlm token-classification generated_from_trainer base_model:microsoft/layoutlm-base-uncased license:mit autotrain_compatible endpoints_compatible region:us