---
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: 0.0582
- Answer: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}
- Header: {'precision': 0.05263157894736842, 'recall': 0.07692307692307693, 'f1': 0.0625, 'number': 13}
- Question: {'precision': 0.1, 'recall': 0.15384615384615385, 'f1': 0.12121212121212123, 'number': 13}
- Overall Precision: 0.0682
- Overall Recall: 0.0882
- Overall F1: 0.0769
- Overall Accuracy: 0.6434

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.1677        | 3.08  | 200  | 0.0239          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13}                                                  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13}                                                 | 0.0               | 0.0            | 0.0        | 0.7295           |
| 0.0578        | 6.15  | 400  | 0.0251          | {'precision': 0.4, 'recall': 0.25, 'f1': 0.3076923076923077, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13}                                                  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13}                                                 | 0.1333            | 0.0588         | 0.0816     | 0.7295           |
| 0.0275        | 9.23  | 600  | 0.0291          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}                 | {'precision': 0.05555555555555555, 'recall': 0.07692307692307693, 'f1': 0.06451612903225808, 'number': 13}  | {'precision': 0.05263157894736842, 'recall': 0.07692307692307693, 'f1': 0.0625, 'number': 13}              | 0.0526            | 0.0588         | 0.0556     | 0.7008           |
| 0.0124        | 12.31 | 800  | 0.0401          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13}                                                  | {'precision': 0.0625, 'recall': 0.07692307692307693, 'f1': 0.06896551724137931, 'number': 13}              | 0.0303            | 0.0294         | 0.0299     | 0.6352           |
| 0.0086        | 15.38 | 1000 | 0.0416          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}                 | {'precision': 0.05263157894736842, 'recall': 0.07692307692307693, 'f1': 0.0625, 'number': 13}               | {'precision': 0.05263157894736842, 'recall': 0.07692307692307693, 'f1': 0.0625, 'number': 13}              | 0.0513            | 0.0588         | 0.0548     | 0.6311           |
| 0.0045        | 18.46 | 1200 | 0.0447          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13}                                                  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13}                                                 | 0.0               | 0.0            | 0.0        | 0.6639           |
| 0.0027        | 21.54 | 1400 | 0.0467          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}                 | {'precision': 0.05, 'recall': 0.07692307692307693, 'f1': 0.060606060606060615, 'number': 13}                | {'precision': 0.09523809523809523, 'recall': 0.15384615384615385, 'f1': 0.11764705882352941, 'number': 13} | 0.0667            | 0.0882         | 0.0759     | 0.6639           |
| 0.0013        | 24.62 | 1600 | 0.0494          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}                 | {'precision': 0.045454545454545456, 'recall': 0.07692307692307693, 'f1': 0.05714285714285715, 'number': 13} | {'precision': 0.08695652173913043, 'recall': 0.15384615384615385, 'f1': 0.1111111111111111, 'number': 13}  | 0.0612            | 0.0882         | 0.0723     | 0.6434           |
| 0.0009        | 27.69 | 1800 | 0.0559          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}                 | {'precision': 0.045454545454545456, 'recall': 0.07692307692307693, 'f1': 0.05714285714285715, 'number': 13} | {'precision': 0.08695652173913043, 'recall': 0.15384615384615385, 'f1': 0.1111111111111111, 'number': 13}  | 0.06              | 0.0882         | 0.0714     | 0.6475           |
| 0.0006        | 30.77 | 2000 | 0.0522          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}                 | {'precision': 0.0625, 'recall': 0.07692307692307693, 'f1': 0.06896551724137931, 'number': 13}               | {'precision': 0.05555555555555555, 'recall': 0.07692307692307693, 'f1': 0.06451612903225808, 'number': 13} | 0.0526            | 0.0588         | 0.0556     | 0.6393           |
| 0.0004        | 33.85 | 2200 | 0.0557          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}                 | {'precision': 0.05263157894736842, 'recall': 0.07692307692307693, 'f1': 0.0625, 'number': 13}               | {'precision': 0.1, 'recall': 0.15384615384615385, 'f1': 0.12121212121212123, 'number': 13}                 | 0.0682            | 0.0882         | 0.0769     | 0.6516           |
| 0.0005        | 36.92 | 2400 | 0.0582          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}                 | {'precision': 0.05263157894736842, 'recall': 0.07692307692307693, 'f1': 0.0625, 'number': 13}               | {'precision': 0.1, 'recall': 0.15384615384615385, 'f1': 0.12121212121212123, 'number': 13}                 | 0.0682            | 0.0882         | 0.0769     | 0.6434           |


### Framework versions

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