---
license: mit
tags:
- generated_from_trainer
model-index:
- name: LILT_on7
  results: []
---

<!-- 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