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# testing_img_token

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: 0.9187
- Eading: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
- Ext: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9}
- Ub heading: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15}
- Overall Precision: 0.0
- Overall Recall: 0.0
- Overall F1: 0.0
- Overall Accuracy: 0.4848

## 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: 0.0005
- 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Eading                                                    | Ext                                                       | Ub heading                                                 | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:----------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.4635        | 1.43  | 10   | 1.2178          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | 0.0               | 0.0            | 0.0        | 0.4848           |
| 1.2206        | 2.86  | 20   | 1.0016          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | 0.0               | 0.0            | 0.0        | 0.4848           |
| 1.0569        | 4.29  | 30   | 0.9783          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | 0.0               | 0.0            | 0.0        | 0.4848           |
| 1.0201        | 5.71  | 40   | 0.9273          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | 0.0               | 0.0            | 0.0        | 0.4848           |
| 0.9888        | 7.14  | 50   | 0.9187          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | 0.0               | 0.0            | 0.0        | 0.4848           |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "testing_img_token", "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 

testing\_img\_token
===================


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: 0.9187
* Eading: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
* Ext: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9}
* Ub heading: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15}
* Overall Precision: 0.0
* Overall Recall: 0.0
* Overall F1: 0.0
* Overall Accuracy: 0.4848


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: 0.0005
* 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: 50


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* 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: 50 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.2
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: 0.0005
* 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: 50 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.2
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: 0.0005
* 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: 50### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.2