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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: 1.5278
- Answer: {'precision': 0.8726415094339622, 'recall': 0.9057527539779682, 'f1': 0.8888888888888888, 'number': 817}
- Header: {'precision': 0.6701030927835051, 'recall': 0.5462184873949579, 'f1': 0.6018518518518517, 'number': 119}
- Question: {'precision': 0.9128440366972477, 'recall': 0.9238625812441968, 'f1': 0.9183202584217812, 'number': 1077}
- Overall Precision: 0.8845
- Overall Recall: 0.8942
- Overall F1: 0.8893
- Overall Accuracy: 0.8213

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4258        | 10.53  | 200  | 0.9877          | {'precision': 0.8354143019296254, 'recall': 0.9008567931456548, 'f1': 0.866902237926973, 'number': 817}  | {'precision': 0.5491803278688525, 'recall': 0.5630252100840336, 'f1': 0.5560165975103735, 'number': 119}  | {'precision': 0.8691756272401434, 'recall': 0.9006499535747446, 'f1': 0.8846329229366164, 'number': 1077} | 0.8367            | 0.8808         | 0.8582     | 0.8030           |
| 0.0396        | 21.05  | 400  | 1.2891          | {'precision': 0.8314855875831486, 'recall': 0.9179926560587516, 'f1': 0.8726003490401396, 'number': 817} | {'precision': 0.5289256198347108, 'recall': 0.5378151260504201, 'f1': 0.5333333333333334, 'number': 119}  | {'precision': 0.9008579599618685, 'recall': 0.8774373259052924, 'f1': 0.8889934148635935, 'number': 1077} | 0.8489            | 0.8738         | 0.8612     | 0.8071           |
| 0.0145        | 31.58  | 600  | 1.1878          | {'precision': 0.8541666666666666, 'recall': 0.9033047735618115, 'f1': 0.8780487804878049, 'number': 817} | {'precision': 0.5943396226415094, 'recall': 0.5294117647058824, 'f1': 0.5599999999999999, 'number': 119}  | {'precision': 0.8793565683646113, 'recall': 0.9136490250696379, 'f1': 0.896174863387978, 'number': 1077}  | 0.8545            | 0.8867         | 0.8703     | 0.8139           |
| 0.0093        | 42.11  | 800  | 1.3968          | {'precision': 0.8727272727272727, 'recall': 0.8812729498164015, 'f1': 0.8769792935444579, 'number': 817} | {'precision': 0.5454545454545454, 'recall': 0.6050420168067226, 'f1': 0.5737051792828685, 'number': 119}  | {'precision': 0.8951686417502279, 'recall': 0.9117920148560817, 'f1': 0.9034038638454462, 'number': 1077} | 0.8637            | 0.8813         | 0.8724     | 0.8054           |
| 0.0042        | 52.63  | 1000 | 1.5509          | {'precision': 0.8372093023255814, 'recall': 0.9253365973072215, 'f1': 0.8790697674418605, 'number': 817} | {'precision': 0.6304347826086957, 'recall': 0.48739495798319327, 'f1': 0.5497630331753555, 'number': 119} | {'precision': 0.9044048734770385, 'recall': 0.8960074280408542, 'f1': 0.9001865671641791, 'number': 1077} | 0.8628            | 0.8838         | 0.8731     | 0.8044           |
| 0.0026        | 63.16  | 1200 | 1.5696          | {'precision': 0.8618266978922716, 'recall': 0.9008567931456548, 'f1': 0.8809096349491322, 'number': 817} | {'precision': 0.6666666666666666, 'recall': 0.5210084033613446, 'f1': 0.5849056603773585, 'number': 119}  | {'precision': 0.8935978358881875, 'recall': 0.9201485608170845, 'f1': 0.9066788655077767, 'number': 1077} | 0.8701            | 0.8887         | 0.8793     | 0.8116           |
| 0.001         | 73.68  | 1400 | 1.7209          | {'precision': 0.8396860986547086, 'recall': 0.9167686658506732, 'f1': 0.8765359859566998, 'number': 817} | {'precision': 0.6781609195402298, 'recall': 0.4957983193277311, 'f1': 0.5728155339805825, 'number': 119}  | {'precision': 0.8969359331476323, 'recall': 0.8969359331476323, 'f1': 0.8969359331476322, 'number': 1077} | 0.8628            | 0.8813         | 0.8720     | 0.7977           |
| 0.0011        | 84.21  | 1600 | 1.5329          | {'precision': 0.8646188850967008, 'recall': 0.9302325581395349, 'f1': 0.8962264150943396, 'number': 817} | {'precision': 0.6666666666666666, 'recall': 0.5042016806722689, 'f1': 0.5741626794258373, 'number': 119}  | {'precision': 0.9050691244239631, 'recall': 0.9117920148560817, 'f1': 0.9084181313598519, 'number': 1077} | 0.8773            | 0.8952         | 0.8862     | 0.8267           |
| 0.0006        | 94.74  | 1800 | 1.5523          | {'precision': 0.8748510131108462, 'recall': 0.8984088127294981, 'f1': 0.8864734299516908, 'number': 817} | {'precision': 0.5811965811965812, 'recall': 0.5714285714285714, 'f1': 0.576271186440678, 'number': 119}   | {'precision': 0.9045412418906394, 'recall': 0.9062209842154132, 'f1': 0.9053803339517627, 'number': 1077} | 0.8737            | 0.8833         | 0.8785     | 0.8196           |
| 0.0005        | 105.26 | 2000 | 1.5178          | {'precision': 0.8758949880668258, 'recall': 0.8984088127294981, 'f1': 0.8870090634441088, 'number': 817} | {'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119}  | {'precision': 0.8995475113122172, 'recall': 0.9229340761374187, 'f1': 0.9110907424381303, 'number': 1077} | 0.8775            | 0.8897         | 0.8836     | 0.8253           |
| 0.0004        | 115.79 | 2200 | 1.5493          | {'precision': 0.8597701149425288, 'recall': 0.9155446756425949, 'f1': 0.8867812685240072, 'number': 817} | {'precision': 0.6631578947368421, 'recall': 0.5294117647058824, 'f1': 0.5887850467289719, 'number': 119}  | {'precision': 0.9107635694572217, 'recall': 0.9192200557103064, 'f1': 0.9149722735674676, 'number': 1077} | 0.8777            | 0.8947         | 0.8861     | 0.8217           |
| 0.0003        | 126.32 | 2400 | 1.5278          | {'precision': 0.8726415094339622, 'recall': 0.9057527539779682, 'f1': 0.8888888888888888, 'number': 817} | {'precision': 0.6701030927835051, 'recall': 0.5462184873949579, 'f1': 0.6018518518518517, 'number': 119}  | {'precision': 0.9128440366972477, 'recall': 0.9238625812441968, 'f1': 0.9183202584217812, 'number': 1077} | 0.8845            | 0.8942         | 0.8893     | 0.8213           |


### 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: 1.5278
* Answer: {'precision': 0.8726415094339622, 'recall': 0.9057527539779682, 'f1': 0.8888888888888888, 'number': 817}
* Header: {'precision': 0.6701030927835051, 'recall': 0.5462184873949579, 'f1': 0.6018518518518517, 'number': 119}
* Question: {'precision': 0.9128440366972477, 'recall': 0.9238625812441968, 'f1': 0.9183202584217812, 'number': 1077}
* Overall Precision: 0.8845
* Overall Recall: 0.8942
* Overall F1: 0.8893
* Overall Accuracy: 0.8213


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