
<!-- 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. -->

# LILT_on7

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: nan
- Able caption: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
- Eading: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62}
- Ext: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102}
- Mage caption: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13}
- Ub heading: {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125}
- Overall Precision: 0.2643
- Overall Recall: 0.4112
- Overall F1: 0.3218
- Overall Accuracy: 0.2643

## 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: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Able caption                                              | Eading                                                     | Ext                                                         | Mage caption                                               | Ub heading                                                                                 | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:----------------------------------------------------------:|:-----------------------------------------------------------:|:----------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.0142        | 0.44  | 500  | nan             | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125} | 0.2643            | 0.4112         | 0.3218     | 0.2643           |
| 1.0228        | 0.89  | 1000 | nan             | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125} | 0.2643            | 0.4112         | 0.3218     | 0.2643           |
| 1.0299        | 1.33  | 1500 | nan             | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125} | 0.2643            | 0.4112         | 0.3218     | 0.2643           |
| 1.0233        | 1.78  | 2000 | nan             | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125} | 0.2643            | 0.4112         | 0.3218     | 0.2643           |
| 0.9924        | 2.22  | 2500 | nan             | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125} | 0.2643            | 0.4112         | 0.3218     | 0.2643           |
| 1.0081        | 2.67  | 3000 | nan             | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125} | 0.2643            | 0.4112         | 0.3218     | 0.2643           |
| 0.9836        | 3.11  | 3500 | nan             | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125} | 0.2643            | 0.4112         | 0.3218     | 0.2643           |
| 0.9997        | 3.56  | 4000 | nan             | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125} | 0.2643            | 0.4112         | 0.3218     | 0.2643           |
| 0.984         | 4.0   | 4500 | nan             | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125} | 0.2643            | 0.4112         | 0.3218     | 0.2643           |
| 0.9889        | 4.44  | 5000 | nan             | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125} | 0.2643            | 0.4112         | 0.3218     | 0.2643           |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "LILT_on7", "results": []}]}
transformers pytorch safetensors lilt token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #lilt #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

LILT\_on7
=========


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: nan
* Able caption: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
* Eading: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62}
* Ext: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102}
* Mage caption: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13}
* Ub heading: {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125}
* Overall Precision: 0.2643
* Overall Recall: 0.4112
* Overall F1: 0.3218
* Overall Accuracy: 0.2643


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: 5000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### 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: 5000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #safetensors #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: 5000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3