
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6973
- Answer: {'precision': 0.8658109684947491, 'recall': 0.9082007343941249, 'f1': 0.886499402628435, 'number': 817}
- Header: {'precision': 0.6770833333333334, 'recall': 0.5462184873949579, 'f1': 0.6046511627906976, 'number': 119}
- Question: {'precision': 0.9074243813015582, 'recall': 0.9192200557103064, 'f1': 0.9132841328413284, 'number': 1077}
- Overall Precision: 0.8792
- Overall Recall: 0.8927
- Overall F1: 0.8859
- Overall Accuracy: 0.8011

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.1857        | 26.32  | 500  | 1.4181          | {'precision': 0.8298109010011123, 'recall': 0.9130966952264382, 'f1': 0.8694638694638694, 'number': 817} | {'precision': 0.6923076923076923, 'recall': 0.5294117647058824, 'f1': 0.5999999999999999, 'number': 119} | {'precision': 0.886672710788758, 'recall': 0.9080779944289693, 'f1': 0.8972477064220182, 'number': 1077}  | 0.8538            | 0.8877         | 0.8704     | 0.7981           |
| 0.0068        | 52.63  | 1000 | 1.6084          | {'precision': 0.8581235697940504, 'recall': 0.9179926560587516, 'f1': 0.8870490833826139, 'number': 817} | {'precision': 0.5877192982456141, 'recall': 0.5630252100840336, 'f1': 0.5751072961373391, 'number': 119} | {'precision': 0.9083255378858747, 'recall': 0.9015784586815228, 'f1': 0.9049394221808015, 'number': 1077} | 0.8692            | 0.8882         | 0.8786     | 0.7956           |
| 0.0018        | 78.95  | 1500 | 1.6068          | {'precision': 0.8742655699177438, 'recall': 0.9106487148102815, 'f1': 0.8920863309352519, 'number': 817} | {'precision': 0.6050420168067226, 'recall': 0.6050420168067226, 'f1': 0.6050420168067226, 'number': 119} | {'precision': 0.902867715078631, 'recall': 0.9062209842154132, 'f1': 0.9045412418906396, 'number': 1077}  | 0.8737            | 0.8902         | 0.8819     | 0.8095           |
| 0.0007        | 105.26 | 2000 | 1.6522          | {'precision': 0.8611111111111112, 'recall': 0.9106487148102815, 'f1': 0.8851873884592504, 'number': 817} | {'precision': 0.6126126126126126, 'recall': 0.5714285714285714, 'f1': 0.591304347826087, 'number': 119}  | {'precision': 0.9098513011152416, 'recall': 0.9090064995357474, 'f1': 0.9094287041337669, 'number': 1077} | 0.8732            | 0.8897         | 0.8814     | 0.8028           |
| 0.0002        | 131.58 | 2500 | 1.6973          | {'precision': 0.8658109684947491, 'recall': 0.9082007343941249, 'f1': 0.886499402628435, 'number': 817}  | {'precision': 0.6770833333333334, 'recall': 0.5462184873949579, 'f1': 0.6046511627906976, 'number': 119} | {'precision': 0.9074243813015582, 'recall': 0.9192200557103064, 'f1': 0.9132841328413284, 'number': 1077} | 0.8792            | 0.8927         | 0.8859     | 0.8011           |


### 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": "SCUT-DLVCLab/lilt-roberta-en-base", "model-index": [{"name": "lilt-en-funsd", "results": []}]}
transformers tensorboard safetensors lilt token-classification generated_from_trainer base_model:SCUT-DLVCLab/lilt-roberta-en-base license:mit autotrain_compatible endpoints_compatible region:us