
<!-- 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. -->

# my-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: 1.7942
- Answer: {'precision': 0.8597914252607184, 'recall': 0.9082007343941249, 'f1': 0.8833333333333333, 'number': 817}
- Header: {'precision': 0.6666666666666666, 'recall': 0.5714285714285714, 'f1': 0.6153846153846153, 'number': 119}
- Question: {'precision': 0.9046746104491292, 'recall': 0.9164345403899722, 'f1': 0.9105166051660516, 'number': 1077}
- Overall Precision: 0.8740
- Overall Recall: 0.8927
- Overall F1: 0.8833
- Overall Accuracy: 0.8042

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.1935        | 26.32  | 500  | 1.2125          | {'precision': 0.8702830188679245, 'recall': 0.9033047735618115, 'f1': 0.8864864864864864, 'number': 817} | {'precision': 0.6296296296296297, 'recall': 0.5714285714285714, 'f1': 0.5991189427312775, 'number': 119} | {'precision': 0.8748921484037964, 'recall': 0.9415041782729805, 'f1': 0.9069767441860466, 'number': 1077} | 0.8605            | 0.9041         | 0.8818     | 0.8024           |
| 0.0063        | 52.63  | 1000 | 1.4406          | {'precision': 0.8732394366197183, 'recall': 0.9106487148102815, 'f1': 0.8915518274415818, 'number': 817} | {'precision': 0.632183908045977, 'recall': 0.46218487394957986, 'f1': 0.5339805825242718, 'number': 119} | {'precision': 0.8827708703374778, 'recall': 0.9229340761374187, 'f1': 0.902405810258738, 'number': 1077}  | 0.8683            | 0.8907         | 0.8794     | 0.8175           |
| 0.002         | 78.95  | 1500 | 1.6624          | {'precision': 0.861904761904762, 'recall': 0.8861689106487148, 'f1': 0.8738684369342186, 'number': 817}  | {'precision': 0.6363636363636364, 'recall': 0.5294117647058824, 'f1': 0.5779816513761468, 'number': 119} | {'precision': 0.8920863309352518, 'recall': 0.9210770659238626, 'f1': 0.9063499314755596, 'number': 1077} | 0.8674            | 0.8838         | 0.8755     | 0.7998           |
| 0.0006        | 105.26 | 2000 | 1.7942          | {'precision': 0.8597914252607184, 'recall': 0.9082007343941249, 'f1': 0.8833333333333333, 'number': 817} | {'precision': 0.6666666666666666, 'recall': 0.5714285714285714, 'f1': 0.6153846153846153, 'number': 119} | {'precision': 0.9046746104491292, 'recall': 0.9164345403899722, 'f1': 0.9105166051660516, 'number': 1077} | 0.8740            | 0.8927         | 0.8833     | 0.8042           |
| 0.0002        | 131.58 | 2500 | 1.8161          | {'precision': 0.8591385331781141, 'recall': 0.9033047735618115, 'f1': 0.8806682577565632, 'number': 817} | {'precision': 0.6346153846153846, 'recall': 0.5546218487394958, 'f1': 0.5919282511210763, 'number': 119} | {'precision': 0.9047619047619048, 'recall': 0.9173630454967502, 'f1': 0.9110189027201475, 'number': 1077} | 0.8720            | 0.8902         | 0.8810     | 0.8021           |


### Framework versions

- Transformers 4.26.0
- 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": "my-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 

my-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: 1.7942
* Answer: {'precision': 0.8597914252607184, 'recall': 0.9082007343941249, 'f1': 0.8833333333333333, 'number': 817}
* Header: {'precision': 0.6666666666666666, 'recall': 0.5714285714285714, 'f1': 0.6153846153846153, 'number': 119}
* Question: {'precision': 0.9046746104491292, 'recall': 0.9164345403899722, 'f1': 0.9105166051660516, 'number': 1077}
* Overall Precision: 0.8740
* Overall Recall: 0.8927
* Overall F1: 0.8833
* Overall Accuracy: 0.8042


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 4.26.0
* 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: 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 4.26.0
* 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: 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 4.26.0
* 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: 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 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2