fine_tuned_yelp_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: 0.2174
• Accuracy: 0.9601

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
0.8575	0.0170	100	0.4890	0.8281
0.7118	0.0340	200	0.5564	0.8484
0.5044	0.0509	300	0.5218	0.8463
0.3969	0.0679	400	0.9168	0.7809
0.4494	0.0849	500	0.5158	0.8453
0.3766	0.1019	600	0.5252	0.8772
0.3938	0.1188	700	0.5676	0.8573
0.3681	0.1358	800	0.2930	0.9116
0.3955	0.1528	900	0.2745	0.8845
0.2859	0.1698	1000	0.3442	0.9150
0.329	0.1868	1100	0.2673	0.9116
0.2898	0.2037	1200	0.1957	0.9221
0.2645	0.2207	1300	0.2623	0.9335
0.2906	0.2377	1400	0.2438	0.9280
0.2511	0.2547	1500	0.3281	0.9211
0.3	0.2716	1600	0.1789	0.9467
0.2592	0.2886	1700	0.2963	0.9270
0.2274	0.3056	1800	0.3387	0.9309
0.2843	0.3226	1900	0.2788	0.9291
0.281	0.3396	2000	0.2553	0.9442
0.2624	0.3565	2100	0.1737	0.9547
0.2503	0.3735	2200	0.1948	0.9454
0.2856	0.3905	2300	0.2797	0.9269
0.1531	0.4075	2400	0.2548	0.9490
0.2316	0.4244	2500	0.2906	0.9440
0.2061	0.4414	2600	0.2194	0.9517
0.1991	0.4584	2700	0.1949	0.9515
0.1721	0.4754	2800	0.2730	0.9368
0.1696	0.4924	2900	0.2238	0.9326
0.2013	0.5093	3000	0.1802	0.9603
0.2026	0.5263	3100	0.2174	0.9601

Framework versions

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