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---
base_model: klue/roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta-base-finetuned-tc
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. -->
# roberta-base-finetuned-tc
This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5510
- Accuracy: 0.8442
- F1: 0.8376
- Precision: 0.8466
- Recall: 0.8442
## 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: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 15 | 1.3600 | 0.5055 | 0.3395 | 0.2556 | 0.5055 |
| No log | 2.0 | 30 | 0.9601 | 0.6712 | 0.5846 | 0.6041 | 0.6712 |
| No log | 3.0 | 45 | 0.7381 | 0.7865 | 0.7621 | 0.7439 | 0.7865 |
| No log | 4.0 | 60 | 0.6402 | 0.8172 | 0.7964 | 0.7793 | 0.8172 |
| No log | 5.0 | 75 | 0.5886 | 0.8258 | 0.8074 | 0.8163 | 0.8258 |
| No log | 6.0 | 90 | 0.5714 | 0.8344 | 0.8213 | 0.8280 | 0.8344 |
| No log | 7.0 | 105 | 0.5618 | 0.8331 | 0.8233 | 0.8386 | 0.8331 |
| No log | 8.0 | 120 | 0.5559 | 0.8380 | 0.8307 | 0.8428 | 0.8380 |
| No log | 9.0 | 135 | 0.5510 | 0.8442 | 0.8376 | 0.8466 | 0.8442 |
| No log | 10.0 | 150 | 0.5545 | 0.8429 | 0.8355 | 0.8454 | 0.8429 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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