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metadata
base_model: klue/roberta-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: roberta-base-finetuned-tc
    results: []

roberta-base-finetuned-tc

This model is a fine-tuned version of 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