---
license: mit
tags:
- generated_from_trainer
model-index:
- name: new_model
  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. -->

# new_model

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: 0.0292
- Answer: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}
- Header: {'precision': 0.07692307692307693, 'recall': 0.058823529411764705, 'f1': 0.06666666666666667, 'number': 17}
- Question: {'precision': 0.0625, 'recall': 0.058823529411764705, 'f1': 0.06060606060606061, 'number': 17}
- Overall Precision: 0.0556
- Overall Recall: 0.0455
- Overall F1: 0.0500
- Overall Accuracy: 0.6578

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                     | Header                                                                                                      | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.1533        | 3.92  | 200  | 0.0173          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}                                                  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}                                                   | 0.0               | 0.0            | 0.0        | 0.5989           |
| 0.0443        | 7.84  | 400  | 0.0137          | {'precision': 0.09090909090909091, 'recall': 0.1, 'f1': 0.09523809523809525, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}                                                  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}                                                   | 0.0164            | 0.0227         | 0.0190     | 0.6203           |
| 0.0194        | 11.76 | 600  | 0.0162          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.2222222222222222, 'recall': 0.11764705882352941, 'f1': 0.15384615384615383, 'number': 17}   | {'precision': 0.07142857142857142, 'recall': 0.058823529411764705, 'f1': 0.06451612903225808, 'number': 17}  | 0.1154            | 0.0682         | 0.0857     | 0.6417           |
| 0.0089        | 15.69 | 800  | 0.0186          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.06666666666666667, 'recall': 0.058823529411764705, 'f1': 0.0625, 'number': 17}              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}                                                   | 0.0278            | 0.0227         | 0.0250     | 0.6684           |
| 0.0036        | 19.61 | 1000 | 0.0264          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.07692307692307693, 'recall': 0.058823529411764705, 'f1': 0.06666666666666667, 'number': 17} | {'precision': 0.0625, 'recall': 0.058823529411764705, 'f1': 0.06060606060606061, 'number': 17}               | 0.0556            | 0.0455         | 0.0500     | 0.6578           |
| 0.0031        | 23.53 | 1200 | 0.0200          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.1111111111111111, 'recall': 0.11764705882352941, 'f1': 0.11428571428571428, 'number': 17}   | {'precision': 0.05263157894736842, 'recall': 0.058823529411764705, 'f1': 0.05555555555555555, 'number': 17}  | 0.0732            | 0.0682         | 0.0706     | 0.6791           |
| 0.0015        | 27.45 | 1400 | 0.0222          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.05, 'recall': 0.058823529411764705, 'f1': 0.05405405405405405, 'number': 17}                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}                                                   | 0.0196            | 0.0227         | 0.0211     | 0.6631           |
| 0.0011        | 31.37 | 1600 | 0.0249          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.05555555555555555, 'recall': 0.058823529411764705, 'f1': 0.05714285714285714, 'number': 17} | {'precision': 0.045454545454545456, 'recall': 0.058823529411764705, 'f1': 0.05128205128205128, 'number': 17} | 0.0408            | 0.0455         | 0.0430     | 0.6845           |
| 0.0006        | 35.29 | 1800 | 0.0243          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.05555555555555555, 'recall': 0.058823529411764705, 'f1': 0.05714285714285714, 'number': 17} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}                                                   | 0.0213            | 0.0227         | 0.0220     | 0.6952           |
| 0.0004        | 39.22 | 2000 | 0.0290          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.07142857142857142, 'recall': 0.058823529411764705, 'f1': 0.06451612903225808, 'number': 17} | {'precision': 0.05555555555555555, 'recall': 0.058823529411764705, 'f1': 0.05714285714285714, 'number': 17}  | 0.0488            | 0.0455         | 0.0471     | 0.6578           |
| 0.0002        | 43.14 | 2200 | 0.0288          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.07142857142857142, 'recall': 0.058823529411764705, 'f1': 0.06451612903225808, 'number': 17} | {'precision': 0.05555555555555555, 'recall': 0.058823529411764705, 'f1': 0.05714285714285714, 'number': 17}  | 0.0488            | 0.0455         | 0.0471     | 0.6578           |
| 0.0002        | 47.06 | 2400 | 0.0292          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                 | {'precision': 0.07692307692307693, 'recall': 0.058823529411764705, 'f1': 0.06666666666666667, 'number': 17} | {'precision': 0.0625, 'recall': 0.058823529411764705, 'f1': 0.06060606060606061, 'number': 17}               | 0.0556            | 0.0455         | 0.0500     | 0.6578           |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.1.0.dev20230810
- Datasets 2.14.4
- Tokenizers 0.11.0