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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: 0.6139
- Answer: {'precision': 0.8076923076923077, 'recall': 0.8739290085679314, 'f1': 0.8395061728395062, 'number': 817}
- Header: {'precision': 0.3148148148148148, 'recall': 0.42857142857142855, 'f1': 0.36298932384341637, 'number': 119}
- Question: {'precision': 0.796595744680851, 'recall': 0.8690807799442897, 'f1': 0.8312611012433393, 'number': 1077}
- Overall Precision: 0.7659
- Overall Recall: 0.8450
- Overall F1: 0.8035
- Overall Accuracy: 0.8009

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 260

### Training results

| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                     | Question                                                                                                 | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.7536        | 2.67  | 200  | 0.6139          | {'precision': 0.8076923076923077, 'recall': 0.8739290085679314, 'f1': 0.8395061728395062, 'number': 817} | {'precision': 0.3148148148148148, 'recall': 0.42857142857142855, 'f1': 0.36298932384341637, 'number': 119} | {'precision': 0.796595744680851, 'recall': 0.8690807799442897, 'f1': 0.8312611012433393, 'number': 1077} | 0.7659            | 0.8450         | 0.8035     | 0.8009           |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- 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: 0.6139
* Answer: {'precision': 0.8076923076923077, 'recall': 0.8739290085679314, 'f1': 0.8395061728395062, 'number': 817}
* Header: {'precision': 0.3148148148148148, 'recall': 0.42857142857142855, 'f1': 0.36298932384341637, 'number': 119}
* Question: {'precision': 0.796595744680851, 'recall': 0.8690807799442897, 'f1': 0.8312611012433393, 'number': 1077}
* Overall Precision: 0.7659
* Overall Recall: 0.8450
* Overall F1: 0.8035
* Overall Accuracy: 0.8009


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: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 260


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 260 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* 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: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 260 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* 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: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 260### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2