d7b9aa6d108d9270340586a36862afb2

This model is a fine-tuned version of distilbert/distilroberta-base on the contemmcm/cls_mmlu dataset. It achieves the following results on the evaluation set:

• Loss: 1.3866
• Data Size: 1.0
• Epoch Runtime: 19.7891
• Accuracy: 0.2527
• F1 Macro: 0.1008

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
• distributed_type: multi-GPU
• num_devices: 4
• total_train_batch_size: 32
• total_eval_batch_size: 32
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: constant
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Data Size	Epoch Runtime	Accuracy	F1 Macro
No log	0	0	1.3929	0	1.2610	0.25	0.1209
No log	1	438	1.3975	0.0078	1.7048	0.2487	0.1590
No log	2	876	1.3891	0.0156	1.6535	0.2453	0.0985
No log	3	1314	1.4083	0.0312	2.0499	0.2533	0.1011
No log	4	1752	1.3941	0.0625	2.6397	0.2487	0.0996
0.0779	5	2190	1.3876	0.125	3.7888	0.2527	0.1008
0.1845	6	2628	1.3869	0.25	6.0474	0.2487	0.0996
1.3878	7	3066	1.3891	0.5	10.7688	0.25	0.1231
1.3892	8.0	3504	1.3862	1.0	19.7412	0.2527	0.1008
1.3863	9.0	3942	1.3877	1.0	19.5814	0.2527	0.1008
1.3885	10.0	4380	1.3868	1.0	19.5196	0.2513	0.1673
1.3861	11.0	4818	1.3877	1.0	19.7914	0.2533	0.1258
1.3882	12.0	5256	1.3866	1.0	19.7891	0.2527	0.1008

Framework versions

• Transformers 4.57.0
• Pytorch 2.8.0+cu128
• Datasets 4.3.0
• Tokenizers 0.22.1