indobert-QA-multiple-choice-fine-tune

This model is a fine-tuned version of indolem/indobert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.4659
• Accuracy: 0.2588
• F1: 0.1064

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: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	1.0	110	2.8199	0.2104	0.1085
No log	2.0	220	2.5135	0.2588	0.1064
No log	3.0	330	2.4995	0.2588	0.1064
No log	4.0	440	2.4478	0.2588	0.1064
2.8208	5.0	550	2.4481	0.2588	0.1064
2.8208	6.0	660	2.4810	0.2588	0.1064
2.8208	7.0	770	2.4651	0.2588	0.1064
2.8208	8.0	880	2.4627	0.2588	0.1064
2.8208	9.0	990	2.4602	0.2588	0.1064
2.4034	10.0	1100	2.4659	0.2588	0.1064

Framework versions

• Transformers 4.49.0
• Pytorch 2.5.1+cu124
• Datasets 3.3.2
• Tokenizers 0.21.0