5b17dd93665cc703eeae45743d93cc85

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

• Loss: 0.6894
• Data Size: 0.125
• Epoch Runtime: 1.0467
• Accuracy: 0.5625
• F1 Macro: 0.36
• Rouge1: 0.5625
• Rouge2: 0.0
• Rougel: 0.5625
• Rougelsum: 0.5625

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	0.6858	0	0.6411	0.5625	0.36	0.5625	0.0	0.5625	0.5625
No log	1	19	0.6886	0.0078	1.3287	0.5625	0.36	0.5625	0.0	0.5625	0.5625
No log	2	38	0.7012	0.0156	0.7284	0.4375	0.3043	0.4375	0.0	0.4375	0.4375
No log	3	57	0.7023	0.0312	0.8211	0.4375	0.3043	0.4375	0.0	0.4375	0.4375
No log	4	76	0.6970	0.0625	1.0438	0.4375	0.3043	0.4375	0.0	0.4375	0.4375
No log	5	95	0.6894	0.125	1.0467	0.5625	0.36	0.5625	0.0	0.5625	0.5625

Framework versions

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