745d0aca53710286a45ede14176833ed

This model is a fine-tuned version of FacebookAI/roberta-large on the google/boolq dataset. It achieves the following results on the evaluation set:

• Loss: 0.6700
• Data Size: 1.0
• Epoch Runtime: 48.0688
• Accuracy: 0.6213
• F1 Macro: 0.3832
• Rouge1: 0.6213
• Rouge2: 0.0
• Rougel: 0.6207
• Rougelsum: 0.6210

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	0.8813	0	4.9280	0.3787	0.2747	0.3787	0.0	0.3793	0.3790
No log	1	294	0.7220	0.0078	5.9251	0.3787	0.2747	0.3787	0.0	0.3793	0.3790
No log	2	588	0.6657	0.0156	5.9677	0.6213	0.3832	0.6213	0.0	0.6207	0.6210
No log	3	882	0.6647	0.0312	7.1606	0.6213	0.3832	0.6213	0.0	0.6207	0.6210
0.0278	4	1176	0.6634	0.0625	8.7207	0.6213	0.3832	0.6213	0.0	0.6207	0.6210
0.056	5	1470	0.6637	0.125	11.5981	0.6213	0.3832	0.6213	0.0	0.6207	0.6210
0.0975	6	1764	0.6690	0.25	17.4131	0.6213	0.3832	0.6213	0.0	0.6207	0.6210
0.6683	7	2058	0.6640	0.5	27.1382	0.6213	0.3832	0.6213	0.0	0.6207	0.6210
0.669	8.0	2352	0.6700	1.0	48.0688	0.6213	0.3832	0.6213	0.0	0.6207	0.6210

Framework versions

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