af90df61e0e39707162c89e6a1f49368

This model is a fine-tuned version of distilbert/distilbert-base-uncased-distilled-squad on the nyu-mll/glue [cola] dataset. It achieves the following results on the evaluation set:

• Loss: 0.6628
• Data Size: 0.125
• Epoch Runtime: 2.0236
• Accuracy: 0.6914
• F1 Macro: 0.4207
• Rouge1: 0.6924
• Rouge2: 0.0
• Rougel: 0.6914
• Rougelsum: 0.6914

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	0.7392	0	0.8850	0.3125	0.2391	0.3115	0.0	0.3125	0.3125
No log	1	267	0.6287	0.0078	1.6666	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
No log	2	534	0.6846	0.0156	1.1551	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
No log	3	801	0.6374	0.0312	1.3000	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
No log	4	1068	0.6354	0.0625	1.5172	0.6885	0.4196	0.6885	0.0	0.6885	0.6885
0.0361	5	1335	0.6628	0.125	2.0236	0.6914	0.4207	0.6924	0.0	0.6914	0.6914

Framework versions

• Transformers 4.57.0
• Pytorch 2.8.0+cu128
• Datasets 4.3.0
• Tokenizers 0.22.1