lmind_nq_train6000_eval6489_v1_reciteonly_qa_v3__home_aiops_zhuty_lm_indexer_data_tyzhu_

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.0955
• Accuracy: 0.7196

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: 1
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 8
• total_train_batch_size: 32
• total_eval_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: constant
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 20.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7054	0.9973	187	0.5535	0.7686
0.4975	2.0	375	0.5416	0.7693
0.422	2.9973	562	0.5611	0.7645
0.3527	4.0	750	0.6100	0.7573
0.2941	4.9973	937	0.6599	0.7522
0.2518	6.0	1125	0.7200	0.7458
0.2138	6.9973	1312	0.7651	0.7421
0.1824	8.0	1500	0.8280	0.7379
0.1481	8.9973	1687	0.8700	0.7355
0.1298	10.0	1875	0.9146	0.7329
0.1167	10.9973	2062	0.9337	0.7309
0.1094	12.0	2250	0.9733	0.7281
0.1052	12.9973	2437	0.9980	0.7266
0.1007	14.0	2625	1.0022	0.7256
0.0971	14.9973	2812	1.0422	0.7234
0.0954	16.0	3000	1.0441	0.7236
0.0888	16.9973	3187	1.0574	0.7223
0.0879	18.0	3375	1.0728	0.7216
0.0879	18.9973	3562	1.0768	0.7200
0.0883	19.9467	3740	1.0955	0.7196

Framework versions

• PEFT 0.5.0
• Transformers 4.40.2
• Pytorch 2.3.0
• Datasets 2.19.1
• Tokenizers 0.19.1