1c96452aebf197af686d9e6df60d497a

This model is a fine-tuned version of albert/albert-large-v1 on the google/boolq dataset. It achieves the following results on the evaluation set:

• Loss: 0.6815
• Data Size: 0.5
• Epoch Runtime: 11.1418
• 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	0.6641	0	2.4491	0.6131	0.4778	0.6127	0.0	0.6121	0.6131
No log	1	294	0.6869	0.0078	2.8044	0.5653	0.4463	0.5650	0.0	0.5650	0.5646
No log	2	588	0.6652	0.0156	2.7644	0.6213	0.3832	0.6213	0.0	0.6207	0.6210
No log	3	882	0.6617	0.0312	3.1004	0.6213	0.3832	0.6213	0.0	0.6207	0.6210
0.0278	4	1176	0.6633	0.0625	3.6760	0.6213	0.3832	0.6213	0.0	0.6207	0.6210
0.0552	5	1470	0.6821	0.125	4.7197	0.6213	0.3832	0.6213	0.0	0.6207	0.6210
0.0973	6	1764	0.6922	0.25	6.7889	0.5260	0.5081	0.5257	0.0	0.5260	0.5260
0.6826	7	2058	0.6815	0.5	11.1418	0.6213	0.3832	0.6213	0.0	0.6207	0.6210

Framework versions

• Transformers 4.57.0
• Pytorch 2.8.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.1