6f2646d029c4c46df8317f6e107501e5

This model is a fine-tuned version of albert/albert-xlarge-v1 on the nyu-mll/glue dataset. It achieves the following results on the evaluation set:

• Loss: 0.6206
• Data Size: 1.0
• Epoch Runtime: 16.6718
• Accuracy: 0.6885
• F1 Macro: 0.4078
• Rouge1: 0.6895
• Rouge2: 0.0
• Rougel: 0.6885
• Rougelsum: 0.6885

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	0.7003	0	1.2168	0.5049	0.4682	0.5049	0.0	0.5039	0.5059
No log	1	267	0.6194	0.0078	2.6476	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
No log	2	534	0.6347	0.0156	1.6012	0.6787	0.5029	0.6797	0.0	0.6797	0.6787
No log	3	801	0.6328	0.0312	1.8524	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
No log	4	1068	0.6075	0.0625	2.2933	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.0371	5	1335	0.6314	0.125	3.2921	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.6152	6	1602	0.6387	0.25	5.1579	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.6193	7	1869	0.6652	0.5	8.9114	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.6032	8.0	2136	0.6206	1.0	16.6718	0.6885	0.4078	0.6895	0.0	0.6885	0.6885

Framework versions

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