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# lilt-en-1k_img

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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1240
- Able: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 20}
- Able caption: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
- Eading: {'precision': 0.1875, 'recall': 0.125, 'f1': 0.15, 'number': 24}
- Ext: {'precision': 0.7962962962962963, 'recall': 0.7962962962962963, 'f1': 0.7962962962962963, 'number': 54}
- Mage: {'precision': 0.5238095238095238, 'recall': 0.6666666666666666, 'f1': 0.5866666666666667, 'number': 33}
- Mage caption: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}
- Ub heading: {'precision': 0.5858585858585859, 'recall': 0.7435897435897436, 'f1': 0.655367231638418, 'number': 78}
- Overall Precision: 0.5860
- Overall Recall: 0.5833
- Overall F1: 0.5847
- Overall Accuracy: 0.6767

## 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 | Able                                                                                        | Able caption                                              | Eading                                                                                                     | Ext                                                                                                     | Mage                                                                                                    | Mage caption                                              | Ub heading                                                                                              | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.8351        | 2.0   | 200  | 1.1577          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 20}                                  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.1724137931034483, 'recall': 0.20833333333333334, 'f1': 0.18867924528301888, 'number': 24}  | {'precision': 0.7321428571428571, 'recall': 0.7592592592592593, 'f1': 0.7454545454545455, 'number': 54} | {'precision': 0.5238095238095238, 'recall': 0.6666666666666666, 'f1': 0.5866666666666667, 'number': 33} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.5662650602409639, 'recall': 0.6025641025641025, 'f1': 0.5838509316770186, 'number': 78} | 0.5476            | 0.5324         | 0.5399     | 0.6567           |
| 0.5827        | 4.0   | 400  | 1.7815          | {'precision': 0.21428571428571427, 'recall': 0.45, 'f1': 0.29032258064516125, 'number': 20} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.23076923076923078, 'recall': 0.125, 'f1': 0.16216216216216217, 'number': 24}               | {'precision': 0.5166666666666667, 'recall': 0.5740740740740741, 'f1': 0.543859649122807, 'number': 54}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.5789473684210527, 'recall': 0.5641025641025641, 'f1': 0.5714285714285715, 'number': 78} | 0.4462            | 0.4028         | 0.4234     | 0.52             |
| 0.4593        | 6.0   | 600  | 1.9964          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 20}                                  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.16666666666666666, 'recall': 0.20833333333333334, 'f1': 0.1851851851851852, 'number': 24}  | {'precision': 0.7547169811320755, 'recall': 0.7407407407407407, 'f1': 0.7476635514018692, 'number': 54} | {'precision': 0.5238095238095238, 'recall': 0.6666666666666666, 'f1': 0.5866666666666667, 'number': 33} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.5641025641025641, 'recall': 0.5641025641025641, 'f1': 0.5641025641025641, 'number': 78} | 0.5441            | 0.5139         | 0.5286     | 0.6467           |
| 0.4241        | 8.0   | 800  | 1.6445          | {'precision': 0.21428571428571427, 'recall': 0.45, 'f1': 0.29032258064516125, 'number': 20} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.23529411764705882, 'recall': 0.16666666666666666, 'f1': 0.19512195121951217, 'number': 24} | {'precision': 0.6666666666666666, 'recall': 0.7407407407407407, 'f1': 0.7017543859649122, 'number': 54} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.6410256410256411, 'recall': 0.6410256410256411, 'f1': 0.6410256410256411, 'number': 78} | 0.515             | 0.4769         | 0.4952     | 0.56             |
| 0.3912        | 10.0  | 1000 | 1.9949          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 20}                                  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.2727272727272727, 'recall': 0.125, 'f1': 0.17142857142857143, 'number': 24}                | {'precision': 0.5892857142857143, 'recall': 0.6111111111111112, 'f1': 0.6, 'number': 54}                | {'precision': 0.5238095238095238, 'recall': 0.6666666666666666, 'f1': 0.5866666666666667, 'number': 33} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.6190476190476191, 'recall': 0.6666666666666666, 'f1': 0.6419753086419754, 'number': 78} | 0.5612            | 0.5093         | 0.5340     | 0.6733           |
| 0.404         | 12.0  | 1200 | 1.8376          | {'precision': 0.21428571428571427, 'recall': 0.45, 'f1': 0.29032258064516125, 'number': 20} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.25, 'recall': 0.16666666666666666, 'f1': 0.2, 'number': 24}                                | {'precision': 0.7592592592592593, 'recall': 0.7592592592592593, 'f1': 0.7592592592592593, 'number': 54} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.6304347826086957, 'recall': 0.7435897435897436, 'f1': 0.6823529411764706, 'number': 78} | 0.5411            | 0.5185         | 0.5296     | 0.5733           |
| 0.3941        | 14.0  | 1400 | 2.1137          | {'precision': 0.21428571428571427, 'recall': 0.45, 'f1': 0.29032258064516125, 'number': 20} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.2727272727272727, 'recall': 0.125, 'f1': 0.17142857142857143, 'number': 24}                | {'precision': 0.6226415094339622, 'recall': 0.6111111111111112, 'f1': 0.616822429906542, 'number': 54}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.625, 'recall': 0.7051282051282052, 'f1': 0.6626506024096386, 'number': 78}              | 0.5102            | 0.4630         | 0.4854     | 0.56             |
| 0.3963        | 16.0  | 1600 | 1.9659          | {'precision': 0.21428571428571427, 'recall': 0.45, 'f1': 0.29032258064516125, 'number': 20} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.375, 'recall': 0.125, 'f1': 0.1875, 'number': 24}                                          | {'precision': 0.6909090909090909, 'recall': 0.7037037037037037, 'f1': 0.6972477064220184, 'number': 54} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.638095238095238, 'recall': 0.8589743589743589, 'f1': 0.7322404371584699, 'number': 78}  | 0.5571            | 0.5417         | 0.5493     | 0.5967           |
| 0.3929        | 18.0  | 1800 | 2.5380          | {'precision': 0.21428571428571427, 'recall': 0.45, 'f1': 0.29032258064516125, 'number': 20} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.25, 'recall': 0.125, 'f1': 0.16666666666666666, 'number': 24}                              | {'precision': 0.5254237288135594, 'recall': 0.5740740740740741, 'f1': 0.5486725663716815, 'number': 54} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.5833333333333334, 'recall': 0.5384615384615384, 'f1': 0.5599999999999999, 'number': 78} | 0.4474            | 0.3935         | 0.4187     | 0.5233           |
| 0.3895        | 20.0  | 2000 | 2.2060          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 20}                                  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.1875, 'recall': 0.125, 'f1': 0.15, 'number': 24}                                           | {'precision': 0.7142857142857143, 'recall': 0.7407407407407407, 'f1': 0.7272727272727273, 'number': 54} | {'precision': 0.5238095238095238, 'recall': 0.6666666666666666, 'f1': 0.5866666666666667, 'number': 33} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.5894736842105263, 'recall': 0.717948717948718, 'f1': 0.6473988439306358, 'number': 78}  | 0.5708            | 0.5602         | 0.5654     | 0.67             |
| 0.3783        | 22.0  | 2200 | 2.2297          | {'precision': 0.21428571428571427, 'recall': 0.45, 'f1': 0.29032258064516125, 'number': 20} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.1875, 'recall': 0.125, 'f1': 0.15, 'number': 24}                                           | {'precision': 0.7142857142857143, 'recall': 0.7407407407407407, 'f1': 0.7272727272727273, 'number': 54} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.5851063829787234, 'recall': 0.7051282051282052, 'f1': 0.6395348837209303, 'number': 78} | 0.5047            | 0.4954         | 0.5        | 0.5467           |
| 0.3833        | 24.0  | 2400 | 2.1240          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 20}                                  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.1875, 'recall': 0.125, 'f1': 0.15, 'number': 24}                                           | {'precision': 0.7962962962962963, 'recall': 0.7962962962962963, 'f1': 0.7962962962962963, 'number': 54} | {'precision': 0.5238095238095238, 'recall': 0.6666666666666666, 'f1': 0.5866666666666667, 'number': 33} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.5858585858585859, 'recall': 0.7435897435897436, 'f1': 0.655367231638418, 'number': 78}  | 0.5860            | 0.5833         | 0.5847     | 0.6767           |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "lilt-en-1k_img", "results": []}]}
transformers pytorch tensorboard lilt token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #lilt #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

lilt-en-1k\_img
===============


This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 2.1240
* Able: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 20}
* Able caption: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
* Eading: {'precision': 0.1875, 'recall': 0.125, 'f1': 0.15, 'number': 24}
* Ext: {'precision': 0.7962962962962963, 'recall': 0.7962962962962963, 'f1': 0.7962962962962963, 'number': 54}
* Mage: {'precision': 0.5238095238095238, 'recall': 0.6666666666666666, 'f1': 0.5866666666666667, 'number': 33}
* Mage caption: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}
* Ub heading: {'precision': 0.5858585858585859, 'recall': 0.7435897435897436, 'f1': 0.655367231638418, 'number': 78}
* Overall Precision: 0.5860
* Overall Recall: 0.5833
* Overall F1: 0.5847
* Overall Accuracy: 0.6767


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 4.28.1
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3



### 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 4.28.1
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #lilt #token-classification #generated_from_trainer #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 4.28.1
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #lilt #token-classification #generated_from_trainer #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 4.28.1
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3