8d1ba8e75c990c4e62f268bb70933545

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

• Loss: 0.6857
• Data Size: 1.0
• Epoch Runtime: 11.8480
• Accuracy: 0.8037
• F1 Macro: 0.7739
• Rouge1: 0.8042
• Rouge2: 0.0
• Rougel: 0.8037
• Rougelsum: 0.8042

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	0.7717	0	1.9257	0.3349	0.2509	0.3343	0.0	0.3355	0.3349
No log	1	114	0.7236	0.0078	3.4428	0.3349	0.2509	0.3343	0.0	0.3355	0.3349
No log	2	228	0.6483	0.0156	2.6689	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
No log	3	342	0.6319	0.0312	3.1471	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
0.0221	4	456	0.6315	0.0625	3.2764	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
0.0221	5	570	0.6802	0.125	3.7588	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
0.0221	6	684	0.5721	0.25	4.9999	0.6651	0.3994	0.6657	0.0	0.6645	0.6651
0.1498	7	798	0.4351	0.5	7.1089	0.7930	0.7727	0.7930	0.0	0.7936	0.7930
0.4609	8.0	912	0.4467	1.0	12.2522	0.8131	0.7777	0.8131	0.0	0.8131	0.8137
0.349	9.0	1026	0.4211	1.0	11.6395	0.8131	0.7941	0.8137	0.0	0.8125	0.8125
0.2872	10.0	1140	0.4588	1.0	12.2964	0.8243	0.8030	0.8243	0.0	0.8243	0.8243
0.2715	11.0	1254	0.6055	1.0	11.6470	0.8101	0.7753	0.8107	0.0	0.8101	0.8101
0.1948	12.0	1368	0.6289	1.0	11.6165	0.7913	0.7360	0.7919	0.0	0.7913	0.7919
0.1688	13.0	1482	0.6857	1.0	11.8480	0.8037	0.7739	0.8042	0.0	0.8037	0.8042

Framework versions

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