---
base_model: klue/roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: model_y3_research_1
  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. -->

# model_y3_research_1

This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9169
- Accuracy: 0.5979
- F1: 0.5435
- Precision: 0.5801
- Recall: 0.5487

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.9798        | 1.0   | 97   | 0.9334          | 0.5833   | 0.4128 | 0.4359    | 0.4577 |
| 0.9489        | 2.0   | 194  | 0.9621          | 0.4792   | 0.2160 | 0.1597    | 0.3333 |
| 0.9564        | 3.0   | 291  | 0.9505          | 0.5104   | 0.3456 | 0.3323    | 0.3764 |
| 0.8319        | 4.0   | 388  | 0.8693          | 0.6458   | 0.5980 | 0.5970    | 0.6167 |
| 0.7045        | 5.0   | 485  | 1.1875          | 0.5729   | 0.4888 | 0.5051    | 0.4891 |
| 0.6337        | 6.0   | 582  | 1.7888          | 0.6042   | 0.4288 | 0.4648    | 0.4752 |
| 0.3682        | 7.0   | 679  | 2.0383          | 0.5521   | 0.4904 | 0.4889    | 0.4967 |
| 0.2195        | 8.0   | 776  | 2.3023          | 0.5625   | 0.4993 | 0.4986    | 0.5055 |
| 0.0244        | 9.0   | 873  | 2.8742          | 0.5417   | 0.4650 | 0.4650    | 0.4674 |
| 0.1459        | 10.0  | 970  | 2.9738          | 0.5521   | 0.4999 | 0.5001    | 0.5157 |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2