d4642015f9fa8db06d31232a6745c19f

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

• Loss: 0.9388
• Data Size: 1.0
• Epoch Runtime: 13.8599
• Accuracy: 0.7215
• F1 Macro: 0.7022
• Rouge1: 0.7218
• Rouge2: 0.0
• Rougel: 0.7209
• Rougelsum: 0.7209

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	Rouge1	Rouge2	Rougel	Rougelsum
No log	0	0	0.7378	0	2.0385	0.3787	0.2747	0.3787	0.0	0.3793	0.3790
No log	1	294	0.6905	0.0078	2.6280	0.5643	0.5106	0.5650	0.0	0.5646	0.5646
No log	2	588	0.6615	0.0156	2.5438	0.6213	0.3832	0.6213	0.0	0.6207	0.6210
No log	3	882	0.6658	0.0312	2.8860	0.6198	0.3857	0.6201	0.0	0.6195	0.6198
0.027	4	1176	0.6602	0.0625	3.4238	0.6213	0.3832	0.6213	0.0	0.6207	0.6210
0.0552	5	1470	0.6547	0.125	4.1016	0.6213	0.3832	0.6213	0.0	0.6207	0.6210
0.0935	6	1764	0.6467	0.25	5.5563	0.6471	0.5745	0.6471	0.0	0.6468	0.6474
0.6078	7	2058	0.6134	0.5	8.3762	0.6838	0.6222	0.6841	0.0	0.6829	0.6841
0.5437	8.0	2352	0.5814	1.0	14.4086	0.6952	0.6273	0.6955	0.0	0.6949	0.6953
0.4424	9.0	2646	0.6237	1.0	13.7459	0.7255	0.6899	0.7255	0.0	0.7255	0.7258
0.293	10.0	2940	0.7580	1.0	13.7696	0.7117	0.7017	0.7119	0.0	0.7114	0.7111
0.1986	11.0	3234	0.8660	1.0	13.5540	0.7390	0.7105	0.7393	0.0	0.7390	0.7384
0.162	12.0	3528	0.9388	1.0	13.8599	0.7215	0.7022	0.7218	0.0	0.7209	0.7209

Framework versions

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