c5923c7e7e1f937f2c2c07fdaa6d0f90

This model is a fine-tuned version of google-bert/bert-large-uncased-whole-word-masking-finetuned-squad on the nyu-mll/glue [cola] dataset. It achieves the following results on the evaluation set:

• Loss: 0.6265
• Data Size: 0.25
• Epoch Runtime: 10.2012
• 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	1.1984	0	1.3500	0.3115	0.2375	0.3105	0.0	0.3115	0.3115
No log	1	267	0.6231	0.0078	1.8006	0.6885	0.4108	0.6885	0.0	0.6885	0.6885
No log	2	534	0.6197	0.0156	2.2162	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
No log	3	801	0.7095	0.0312	3.1023	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
No log	4	1068	0.6285	0.0625	4.3524	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.0382	5	1335	0.6905	0.125	6.6656	0.6885	0.4078	0.6895	0.0	0.6885	0.6885
0.6121	6	1602	0.6265	0.25	10.2012	0.6885	0.4078	0.6895	0.0	0.6885	0.6885

Framework versions

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