5128385342b49e3f7be0ae7263d1c79e

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

• Loss: 0.6220
• Data Size: 1.0
• Epoch Runtime: 18.3263
• 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	0.6854	0	1.2437	0.6113	0.5675	0.6113	0.0	0.6113	0.6113
No log	1	267	0.6257	0.0078	1.9318	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
No log	2	534	0.6242	0.0156	1.7183	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
No log	3	801	0.6690	0.0312	2.0585	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
No log	4	1068	0.6735	0.0625	2.5321	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.0372	5	1335	0.6490	0.125	3.4723	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.6185	6	1602	0.6228	0.25	5.5109	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.6323	7	1869	0.6557	0.5	9.7213	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.6073	8.0	2136	0.6204	1.0	18.1081	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.6074	9.0	2403	0.6383	1.0	17.8469	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.6173	10.0	2670	0.6205	1.0	18.1830	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.6169	11.0	2937	0.6224	1.0	18.1427	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.6235	12.0	3204	0.6203	1.0	18.5313	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.6124	13.0	3471	0.6211	1.0	18.5623	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.6239	14.0	3738	0.6211	1.0	18.3426	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.5877	15.0	4005	0.6302	1.0	18.1930	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.6214	16.0	4272	0.6220	1.0	18.3263	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