gpt2-medium-wikitext

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 3.1666
• Accuracy: 0.4218
• Perplexity: 23.7262
• Bleu: 0.1462

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: 0.0001
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Perplexity	Bleu
6.078	0.2806	500	5.9534	0.1875	385.0606	0.0310
5.0653	0.5612	1000	4.9232	0.2616	137.4410	0.0633
4.3357	0.8418	1500	4.2163	0.3222	67.7828	0.0857
3.9453	1.1223	2000	3.8824	0.3534	48.5418	0.1107
3.7572	1.4029	2500	3.7058	0.3684	40.6810	0.1217
3.6475	1.6835	3000	3.5827	0.3788	35.9700	0.1306
3.5431	1.9641	3500	3.4927	0.3878	32.8733	0.1347
3.4221	2.2447	4000	3.4283	0.3939	30.8231	0.1356
3.36	2.5253	4500	3.3719	0.3996	29.1351	0.1384
3.3281	2.8058	5000	3.3257	0.4041	27.8193	0.1369
3.2071	3.0864	5500	3.2885	0.4080	26.8024	0.1442
3.2002	3.3670	6000	3.2594	0.4117	26.0335	0.1477
3.1778	3.6476	6500	3.2319	0.4142	25.3278	0.1436
3.1523	3.9282	7000	3.2091	0.4167	24.7565	0.1462
3.0842	4.2088	7500	3.1917	0.4185	24.3289	0.1434
3.0465	4.4893	8000	3.1789	0.4201	24.0197	0.1460
3.0563	4.7699	8500	3.1666	0.4218	23.7262	0.1462

Framework versions

• Transformers 4.49.0
• Pytorch 2.6.0+cu124
• Datasets 3.3.2
• Tokenizers 0.21.0