---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: CN_RoBERTa_Dig
  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. -->

# CN_RoBERTa_Dig

This model is a fine-tuned version of [roberta-base](https://huggingface.co./roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0055
- F1: {'f1': 0.9988009592326139}
- Accuracy: {'accuracy': 0.9988}

## 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: 1e-05
- 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
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1                         | Accuracy             |
|:-------------:|:-----:|:-----:|:---------------:|:--------------------------:|:--------------------:|
| 0.4018        | 0.09  | 1000  | 0.3457          | {'f1': 0.6695906432748538} | {'accuracy': 0.7514} |
| 0.3392        | 0.18  | 2000  | 0.2601          | {'f1': 0.9148995796356842} | {'accuracy': 0.9089} |
| 0.2443        | 0.27  | 3000  | 0.1276          | {'f1': 0.9713375796178344} | {'accuracy': 0.9712} |
| 0.1399        | 0.36  | 4000  | 0.0616          | {'f1': 0.9867973594718943} | {'accuracy': 0.9868} |
| 0.0926        | 0.44  | 5000  | 0.0280          | {'f1': 0.9927341494973624} | {'accuracy': 0.9927} |
| 0.0835        | 0.53  | 6000  | 0.0260          | {'f1': 0.9942196531791908} | {'accuracy': 0.9942} |
| 0.0617        | 0.62  | 7000  | 0.0129          | {'f1': 0.9969981989193516} | {'accuracy': 0.997}  |
| 0.0459        | 0.71  | 8000  | 0.0097          | {'f1': 0.9977029861180465} | {'accuracy': 0.9977} |
| 0.0363        | 0.8   | 9000  | 0.0111          | {'f1': 0.9976047904191618} | {'accuracy': 0.9976} |
| 0.0421        | 0.89  | 10000 | 0.0078          | {'f1': 0.9980035935316429} | {'accuracy': 0.998}  |
| 0.0317        | 0.98  | 11000 | 0.0055          | {'f1': 0.9988009592326139} | {'accuracy': 0.9988} |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0