8d5a2dba24789d9b43ebaac25b1ee240

This model is a fine-tuned version of FacebookAI/roberta-large on the nyu-mll/glue [mrpc] dataset. It achieves the following results on the evaluation set:

• Loss: 0.5190
• Data Size: 1.0
• Epoch Runtime: 21.5616
• Accuracy: 0.8096
• F1 Macro: 0.7745
• Rouge1: 0.8096
• Rouge2: 0.0
• Rougel: 0.8096
• Rougelsum: 0.8090

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	0.8274	0	2.8828	0.3349	0.2509	0.3343	0.0	0.3355	0.3349
No log	1	114	0.7001	0.0078	3.1883	0.4062	0.4058	0.4057	0.0	0.4068	0.4062
No log	2	228	0.8265	0.0156	4.1266	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
No log	3	342	0.7380	0.0312	5.0441	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
0.0226	4	456	0.6586	0.0625	5.8597	0.6645	0.5565	0.6648	0.0	0.6639	0.6639
0.0226	5	570	0.6333	0.125	6.9964	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
0.0226	6	684	0.5908	0.25	9.4584	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
0.1514	7	798	0.5952	0.5	13.9365	0.6144	0.6144	0.6138	0.0	0.6144	0.6150
0.5746	8.0	912	0.5381	1.0	22.6861	0.7730	0.7332	0.7730	0.0	0.7730	0.7730
0.5472	9.0	1026	0.5099	1.0	21.8448	0.7919	0.7510	0.7919	0.0	0.7919	0.7919
0.4798	10.0	1140	0.4725	1.0	21.7458	0.7524	0.7480	0.7524	0.0	0.7524	0.7524
0.4815	11.0	1254	0.5806	1.0	21.5953	0.7541	0.6538	0.7547	0.0	0.7547	0.7541
0.4838	12.0	1368	0.4759	1.0	21.6860	0.8084	0.7875	0.8084	0.0	0.8084	0.8090
0.413	13.0	1482	0.5221	1.0	21.4649	0.7972	0.7448	0.7966	0.0	0.7978	0.7972
0.3881	14.0	1596	0.4716	1.0	22.3045	0.8037	0.7832	0.8042	0.0	0.8037	0.8037
0.4679	15.0	1710	0.5570	1.0	21.2742	0.75	0.6321	0.75	0.0	0.7506	0.75
0.4454	16.0	1824	0.4747	1.0	21.6843	0.8060	0.7792	0.8066	0.0	0.8060	0.8060
0.406	17.0	1938	0.5561	1.0	22.2846	0.8072	0.7732	0.8075	0.0	0.8066	0.8075
0.416	18.0	2052	0.5190	1.0	21.5616	0.8096	0.7745	0.8096	0.0	0.8096	0.8090

Framework versions

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