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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: 1.2479
- Answer: {'precision': 0.8644859813084113, 'recall': 0.9057527539779682, 'f1': 0.8846383741781233, 'number': 817}
- Header: {'precision': 0.6262626262626263, 'recall': 0.5210084033613446, 'f1': 0.5688073394495413, 'number': 119}
- Question: {'precision': 0.8877005347593583, 'recall': 0.924791086350975, 'f1': 0.9058663028649386, 'number': 1077}
- Overall Precision: 0.8657
- Overall Recall: 0.8932
- Overall F1: 0.8792
- Overall Accuracy: 0.8133

## 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: 500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                    | Question                                                                                                 | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4245        | 10.53 | 200  | 0.9942          | {'precision': 0.8187845303867404, 'recall': 0.9069767441860465, 'f1': 0.8606271777003485, 'number': 817} | {'precision': 0.5178571428571429, 'recall': 0.48739495798319327, 'f1': 0.5021645021645021, 'number': 119} | {'precision': 0.8821396192203083, 'recall': 0.903435468895079, 'f1': 0.8926605504587157, 'number': 1077} | 0.8358            | 0.8803         | 0.8575     | 0.8150           |
| 0.0366        | 21.05 | 400  | 1.2479          | {'precision': 0.8644859813084113, 'recall': 0.9057527539779682, 'f1': 0.8846383741781233, 'number': 817} | {'precision': 0.6262626262626263, 'recall': 0.5210084033613446, 'f1': 0.5688073394495413, 'number': 119}  | {'precision': 0.8877005347593583, 'recall': 0.924791086350975, 'f1': 0.9058663028649386, 'number': 1077} | 0.8657            | 0.8932         | 0.8792     | 0.8133           |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"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: 1.2479
* Answer: {'precision': 0.8644859813084113, 'recall': 0.9057527539779682, 'f1': 0.8846383741781233, 'number': 817}
* Header: {'precision': 0.6262626262626263, 'recall': 0.5210084033613446, 'f1': 0.5688073394495413, 'number': 119}
* Question: {'precision': 0.8877005347593583, 'recall': 0.924791086350975, 'f1': 0.9058663028649386, 'number': 1077}
* Overall Precision: 0.8657
* Overall Recall: 0.8932
* Overall F1: 0.8792
* Overall Accuracy: 0.8133


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: 500
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### 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: 500
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
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: 500
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
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: 500
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2