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# lilt-en-funsd

This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4588
- Answer: {'precision': 0.8786057692307693, 'recall': 0.8947368421052632, 'f1': 0.88659793814433, 'number': 817}
- Header: {'precision': 0.6442307692307693, 'recall': 0.5630252100840336, 'f1': 0.600896860986547, 'number': 119}
- Question: {'precision': 0.8854351687388987, 'recall': 0.9257195914577531, 'f1': 0.9051293690422152, 'number': 1077}
- Overall Precision: 0.8705
- Overall Recall: 0.8917
- Overall F1: 0.8810
- Overall Accuracy: 0.8222

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                    | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4177        | 10.53  | 200  | 0.9741          | {'precision': 0.834106728538283, 'recall': 0.8800489596083231, 'f1': 0.8564621798689696, 'number': 817}  | {'precision': 0.6363636363636364, 'recall': 0.4117647058823529, 'f1': 0.5, 'number': 119}                 | {'precision': 0.8831985624438454, 'recall': 0.9127205199628597, 'f1': 0.897716894977169, 'number': 1077}  | 0.8533            | 0.8698         | 0.8615     | 0.8139           |
| 0.0528        | 21.05  | 400  | 1.2793          | {'precision': 0.8391845979614949, 'recall': 0.9069767441860465, 'f1': 0.871764705882353, 'number': 817}  | {'precision': 0.5480769230769231, 'recall': 0.4789915966386555, 'f1': 0.5112107623318385, 'number': 119}  | {'precision': 0.878645343367827, 'recall': 0.8672237697307336, 'f1': 0.8728971962616822, 'number': 1077}  | 0.8449            | 0.8604         | 0.8526     | 0.8057           |
| 0.0154        | 31.58  | 600  | 1.3635          | {'precision': 0.8705463182897862, 'recall': 0.8971848225214198, 'f1': 0.8836648583484027, 'number': 817} | {'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119}  | {'precision': 0.8776041666666666, 'recall': 0.9387186629526463, 'f1': 0.9071332436069988, 'number': 1077} | 0.8638            | 0.8977         | 0.8804     | 0.8164           |
| 0.0082        | 42.11  | 800  | 1.4185          | {'precision': 0.8700361010830325, 'recall': 0.8849449204406364, 'f1': 0.8774271844660194, 'number': 817} | {'precision': 0.6428571428571429, 'recall': 0.6050420168067226, 'f1': 0.6233766233766234, 'number': 119}  | {'precision': 0.8921124206708976, 'recall': 0.9136490250696379, 'f1': 0.9027522935779816, 'number': 1077} | 0.8695            | 0.8838         | 0.8766     | 0.8212           |
| 0.0038        | 52.63  | 1000 | 1.4588          | {'precision': 0.8786057692307693, 'recall': 0.8947368421052632, 'f1': 0.88659793814433, 'number': 817}   | {'precision': 0.6442307692307693, 'recall': 0.5630252100840336, 'f1': 0.600896860986547, 'number': 119}   | {'precision': 0.8854351687388987, 'recall': 0.9257195914577531, 'f1': 0.9051293690422152, 'number': 1077} | 0.8705            | 0.8917         | 0.8810     | 0.8222           |
| 0.0026        | 63.16  | 1200 | 1.5730          | {'precision': 0.8666666666666667, 'recall': 0.8910648714810282, 'f1': 0.8786964393482196, 'number': 817} | {'precision': 0.7073170731707317, 'recall': 0.48739495798319327, 'f1': 0.5771144278606964, 'number': 119} | {'precision': 0.8887884267631103, 'recall': 0.9127205199628597, 'f1': 0.9005955107650022, 'number': 1077} | 0.8723            | 0.8788         | 0.8755     | 0.8139           |
| 0.0015        | 73.68  | 1400 | 1.6294          | {'precision': 0.837471783295711, 'recall': 0.9082007343941249, 'f1': 0.8714034057545508, 'number': 817}  | {'precision': 0.6530612244897959, 'recall': 0.5378151260504201, 'f1': 0.5898617511520737, 'number': 119}  | {'precision': 0.9039179104477612, 'recall': 0.8997214484679665, 'f1': 0.9018147975802697, 'number': 1077} | 0.8633            | 0.8818         | 0.8725     | 0.8173           |
| 0.001         | 84.21  | 1600 | 1.6406          | {'precision': 0.8434684684684685, 'recall': 0.9167686658506732, 'f1': 0.8785923753665689, 'number': 817} | {'precision': 0.6260869565217392, 'recall': 0.6050420168067226, 'f1': 0.6153846153846154, 'number': 119}  | {'precision': 0.9001865671641791, 'recall': 0.8960074280408542, 'f1': 0.8980921358771522, 'number': 1077} | 0.8607            | 0.8872         | 0.8738     | 0.8140           |
| 0.0006        | 94.74  | 1800 | 1.6743          | {'precision': 0.8525714285714285, 'recall': 0.9130966952264382, 'f1': 0.8817966903073285, 'number': 817} | {'precision': 0.6666666666666666, 'recall': 0.5042016806722689, 'f1': 0.5741626794258373, 'number': 119}  | {'precision': 0.8982584784601283, 'recall': 0.9099350046425255, 'f1': 0.904059040590406, 'number': 1077}  | 0.8687            | 0.8872         | 0.8779     | 0.8082           |
| 0.0003        | 105.26 | 2000 | 1.7003          | {'precision': 0.8696682464454977, 'recall': 0.8984088127294981, 'f1': 0.8838049367850691, 'number': 817} | {'precision': 0.6404494382022472, 'recall': 0.4789915966386555, 'f1': 0.548076923076923, 'number': 119}   | {'precision': 0.8927272727272727, 'recall': 0.9117920148560817, 'f1': 0.9021589343132751, 'number': 1077} | 0.8721            | 0.8808         | 0.8764     | 0.8110           |
| 0.0002        | 115.79 | 2200 | 1.7767          | {'precision': 0.8564867967853043, 'recall': 0.9130966952264382, 'f1': 0.8838862559241707, 'number': 817} | {'precision': 0.64, 'recall': 0.5378151260504201, 'f1': 0.5844748858447488, 'number': 119}                | {'precision': 0.9077212806026366, 'recall': 0.8950789229340761, 'f1': 0.9013557737260401, 'number': 1077} | 0.8726            | 0.8813         | 0.8769     | 0.8004           |
| 0.0002        | 126.32 | 2400 | 1.7093          | {'precision': 0.8546910755148741, 'recall': 0.9143206854345165, 'f1': 0.8835008870490834, 'number': 817} | {'precision': 0.6413043478260869, 'recall': 0.4957983193277311, 'f1': 0.5592417061611374, 'number': 119}  | {'precision': 0.8956602031394275, 'recall': 0.9006499535747446, 'f1': 0.8981481481481481, 'number': 1077} | 0.8668            | 0.8823         | 0.8744     | 0.8027           |


### Framework versions

- Transformers 4.28.1
- Pytorch 1.13.1+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["funsd-layoutlmv3"], "model-index": [{"name": "lilt-en-funsd", "results": []}]}
transformers pytorch tensorboard lilt token-classification generated_from_trainer dataset:funsd-layoutlmv3 license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #lilt #token-classification #generated_from_trainer #dataset-funsd-layoutlmv3 #license-mit #autotrain_compatible #endpoints_compatible #region-us 

lilt-en-funsd
=============


This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the funsd-layoutlmv3 dataset.
It achieves the following results on the evaluation set:


* Loss: 1.4588
* Answer: {'precision': 0.8786057692307693, 'recall': 0.8947368421052632, 'f1': 0.88659793814433, 'number': 817}
* Header: {'precision': 0.6442307692307693, 'recall': 0.5630252100840336, 'f1': 0.600896860986547, 'number': 119}
* Question: {'precision': 0.8854351687388987, 'recall': 0.9257195914577531, 'f1': 0.9051293690422152, 'number': 1077}
* Overall Precision: 0.8705
* Overall Recall: 0.8917
* Overall F1: 0.8810
* Overall Accuracy: 0.8222


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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 2500
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 1.13.1+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 2500
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 1.13.1+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #lilt #token-classification #generated_from_trainer #dataset-funsd-layoutlmv3 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 2500
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 1.13.1+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #lilt #token-classification #generated_from_trainer #dataset-funsd-layoutlmv3 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 2500
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 1.13.1+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3