c4c1d99ced08249334701f7cd0768d7e

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

• Loss: 1.1046
• Data Size: 0.25
• Epoch Runtime: 286.3061
• Accuracy: 0.3182
• F1 Macro: 0.1609
• Rouge1: 0.3184
• Rouge2: 0.0
• Rougel: 0.3182
• Rougelsum: 0.3183

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	1.1462	0	7.8372	0.3472	0.2742	0.3472	0.0	0.3472	0.3473
1.1458	1	12271	1.1202	0.0078	17.7066	0.3273	0.1644	0.3273	0.0	0.3275	0.3277
1.114	2	24542	1.0975	0.0156	26.7801	0.3545	0.1745	0.3544	0.0	0.3545	0.3543
1.1266	3	36813	1.0998	0.0312	44.3000	0.3545	0.1745	0.3544	0.0	0.3545	0.3543
1.1132	4	49084	1.0978	0.0625	79.6510	0.3545	0.1745	0.3544	0.0	0.3545	0.3543
1.1094	5	61355	1.0992	0.125	148.0171	0.3545	0.1745	0.3544	0.0	0.3545	0.3543
1.1016	6	73626	1.1046	0.25	286.3061	0.3182	0.1609	0.3184	0.0	0.3182	0.3183

Framework versions

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