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# lilt-en-funsd-custom

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 mydata dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0023
- In: {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2}
- Ear: {'precision': 0.6666666666666666, 'recall': 1.0, 'f1': 0.8, 'number': 2}
- Overall Precision: 0.6
- Overall Recall: 0.75
- Overall F1: 0.6667
- Overall Accuracy: 0.9984

## 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: 200
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | In                                                        | Ear                                                                      | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.1628        | 25.0  | 50   | 0.0023          | {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2} | {'precision': 0.6666666666666666, 'recall': 1.0, 'f1': 0.8, 'number': 2} | 0.6               | 0.75           | 0.6667     | 0.9984           |
| 0.0002        | 50.0  | 100  | 0.0015          | {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2} | {'precision': 0.6666666666666666, 'recall': 1.0, 'f1': 0.8, 'number': 2} | 0.6               | 0.75           | 0.6667     | 0.9984           |
| 0.0001        | 75.0  | 150  | 0.0020          | {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2} | {'precision': 0.6666666666666666, 'recall': 1.0, 'f1': 0.8, 'number': 2} | 0.6               | 0.75           | 0.6667     | 0.9984           |
| 0.0           | 100.0 | 200  | 0.0020          | {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2} | {'precision': 0.6666666666666666, 'recall': 1.0, 'f1': 0.8, 'number': 2} | 0.6               | 0.75           | 0.6667     | 0.9984           |


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 1.8.0+cu101
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["mydata"], "model-index": [{"name": "lilt-en-funsd-custom", "results": []}]}
transformers pytorch tensorboard lilt token-classification generated_from_trainer dataset:mydata license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #lilt #token-classification #generated_from_trainer #dataset-mydata #license-mit #autotrain_compatible #endpoints_compatible #region-us 

lilt-en-funsd-custom
====================


This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the mydata dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0023
* In: {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2}
* Ear: {'precision': 0.6666666666666666, 'recall': 1.0, 'f1': 0.8, 'number': 2}
* Overall Precision: 0.6
* Overall Recall: 0.75
* Overall F1: 0.6667
* Overall Accuracy: 0.9984


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: 200
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 1.8.0+cu101
* Datasets 2.12.0
* Tokenizers 0.13.3



### 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: 200
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 1.8.0+cu101
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #lilt #token-classification #generated_from_trainer #dataset-mydata #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: 200
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 1.8.0+cu101
* Datasets 2.12.0
* Tokenizers 0.13.3