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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: 0.8047
- Answer: {'precision': 0.5882917466410749, 'recall': 0.7503059975520195, 'f1': 0.6594943518020442, 'number': 817}
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
- Question: {'precision': 0.629838142153413, 'recall': 0.8310120705663882, 'f1': 0.7165732586068856, 'number': 1077}
- Overall Precision: 0.6044
- Overall Recall: 0.7491
- Overall F1: 0.6690
- Overall Accuracy: 0.7169

## 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: 25

### Training results



### Framework versions

- Transformers 4.28.1
- Pytorch 1.13.0+cpu
- 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: 0.8047
- Answer: {'precision': 0.5882917466410749, 'recall': 0.7503059975520195, 'f1': 0.6594943518020442, 'number': 817}
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
- Question: {'precision': 0.629838142153413, 'recall': 0.8310120705663882, 'f1': 0.7165732586068856, 'number': 1077}
- Overall Precision: 0.6044
- Overall Recall: 0.7491
- Overall F1: 0.6690
- Overall Accuracy: 0.7169

## 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: 25

### Training results



### Framework versions

- Transformers 4.28.1
- Pytorch 1.13.0+cpu
- Datasets 2.11.0
- Tokenizers 0.13.3

# 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: 0.8047
- Answer: {'precision': 0.5882917466410749, 'recall': 0.7503059975520195, 'f1': 0.6594943518020442, 'number': 817}
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
- Question: {'precision': 0.629838142153413, 'recall': 0.8310120705663882, 'f1': 0.7165732586068856, 'number': 1077}
- Overall Precision: 0.6044
- Overall Recall: 0.7491
- Overall F1: 0.6690
- Overall Accuracy: 0.7169 ## 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: 25 ### Training results ### Framework versions

- Transformers 4.28.1
- Pytorch 1.13.0+cpu
- 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 
 # 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: 0.8047
- Answer: {'precision': 0.5882917466410749, 'recall': 0.7503059975520195, 'f1': 0.6594943518020442, 'number': 817}
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
- Question: {'precision': 0.629838142153413, 'recall': 0.8310120705663882, 'f1': 0.7165732586068856, 'number': 1077}
- Overall Precision: 0.6044
- Overall Recall: 0.7491
- Overall F1: 0.6690
- Overall Accuracy: 0.7169 ## 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: 25 ### Training results ### Framework versions

- Transformers 4.28.1
- Pytorch 1.13.0+cpu
- 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 
# 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: 0.8047
- Answer: {'precision': 0.5882917466410749, 'recall': 0.7503059975520195, 'f1': 0.6594943518020442, 'number': 817}
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
- Question: {'precision': 0.629838142153413, 'recall': 0.8310120705663882, 'f1': 0.7165732586068856, 'number': 1077}
- Overall Precision: 0.6044
- Overall Recall: 0.7491
- Overall F1: 0.6690
- Overall Accuracy: 0.7169## 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: 25### Training results### Framework versions

- Transformers 4.28.1
- Pytorch 1.13.0+cpu
- Datasets 2.11.0
- Tokenizers 0.13.3