dfdd7f251470b0f68d5b949900ec54eb

This model is a fine-tuned version of albert/albert-xlarge-v2 on the google/boolq dataset. It achieves the following results on the evaluation set:

• Loss: 0.6856
• Data Size: 0.25
• Epoch Runtime: 20.5304
• 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	0.8292	0	6.7788	0.3787	0.2747	0.3787	0.0	0.3793	0.3790
No log	1	294	0.7233	0.0078	7.4412	0.3787	0.2747	0.3787	0.0	0.3793	0.3790
No log	2	588	0.6642	0.0156	7.8401	0.6213	0.3832	0.6213	0.0	0.6207	0.6210
No log	3	882	0.6795	0.0312	8.7454	0.6213	0.3832	0.6213	0.0	0.6207	0.6210
0.0279	4	1176	0.6664	0.0625	10.4650	0.6213	0.3832	0.6213	0.0	0.6207	0.6210
0.0559	5	1470	0.6668	0.125	13.7235	0.6213	0.3832	0.6213	0.0	0.6207	0.6210
0.0978	6	1764	0.6856	0.25	20.5304	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