fd93f4990f1fa74f057615fdb7a5c4ff

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

• Loss: 0.7336
• Data Size: 0.125
• Epoch Runtime: 3.0510
• Accuracy: 0.6651
• F1 Macro: 0.3994
• Rouge1: 0.6657
• Rouge2: 0.0
• Rougel: 0.6645
• Rougelsum: 0.6651

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	0.8789	0	1.8005	0.3349	0.2509	0.3343	0.0	0.3355	0.3349
No log	1	114	0.6503	0.0078	2.6914	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
No log	2	228	0.7136	0.0156	1.9829	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
No log	3	342	0.7620	0.0312	2.2842	0.5006	0.4300	0.5006	0.0	0.5006	0.5006
0.0225	4	456	0.9375	0.0625	2.4244	0.3349	0.2509	0.3343	0.0	0.3355	0.3349
0.0225	5	570	0.7336	0.125	3.0510	0.6651	0.3994	0.6657	0.0	0.6645	0.6651

Framework versions

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