gigazine-labeling

This model is a fine-tuned version of tohoku-nlp/bert-base-japanese-v3 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.3062
• Accuracy: 0.623

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_ratio: 0.1
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.4089	1.0	125	1.5892	0.551
1.2661	2.0	250	1.3291	0.601
0.6811	3.0	375	1.3062	0.623

Framework versions

• Transformers 4.43.4
• Pytorch 2.4.0+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1