fine_tuned_sci_gen_callback10

This model is a fine-tuned version of Qwen/Qwen2-1.5B on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.1471
• Accuracy: 0.9736

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: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• 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
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.8103	0.0384	100	0.4049	0.8802
0.3921	0.0769	200	0.4732	0.9001
0.4581	0.1153	300	0.2876	0.9256
0.2995	0.1537	400	0.3032	0.9313
0.2645	0.1922	500	0.3322	0.9334
0.2994	0.2306	600	0.1808	0.9494
0.2834	0.2690	700	0.2584	0.9464
0.3254	0.3075	800	0.2653	0.9408
0.2211	0.3459	900	0.1439	0.9585
0.1954	0.3843	1000	0.1905	0.9520
0.2486	0.4228	1100	0.2971	0.9321
0.1363	0.4612	1200	0.2809	0.9386
0.2191	0.4996	1300	0.2436	0.9477
0.2003	0.5380	1400	0.1320	0.9667
0.1653	0.5765	1500	0.1779	0.9602
0.1465	0.6149	1600	0.1386	0.9650
0.1698	0.6533	1700	0.1004	0.9663
0.1545	0.6918	1800	0.1772	0.9598
0.1633	0.7302	1900	0.1856	0.9555
0.1889	0.7686	2000	0.1754	0.9680
0.214	0.8071	2100	0.1801	0.9503
0.1636	0.8455	2200	0.1771	0.9641
0.1469	0.8839	2300	0.1493	0.9650
0.195	0.9224	2400	0.1066	0.9693
0.1109	0.9608	2500	0.1461	0.9684
0.1544	0.9992	2600	0.1412	0.9650
0.0386	1.0377	2700	0.1471	0.9736

Framework versions

• Transformers 4.49.0
• Pytorch 2.6.0+cu126
• Datasets 3.3.2
• Tokenizers 0.21.0