              precision    recall  f1-score   support

           0       0.78      0.49      0.60       100
           1       0.32      0.38      0.35       100
           2       0.29      0.51      0.37       100
           3       0.24      0.34      0.28       100
           4       0.35      0.16      0.22       100
           5       1.00      0.04      0.08        50

    accuracy                           0.35       550
   macro avg       0.49      0.32      0.32       550
weighted avg       0.45      0.35      0.34       550

Confusion Matrix:

[[49 43  5  3  0  0]
 [12 38 42  7  1  0]
 [ 2 30 51 17  0  0]
 [ 0  8 47 34 11  0]
 [ 0  1 24 59 16  0]
 [ 0  0  6 24 18  2]]

Test metrics

 epoch                   =       20.0
 eval_accuracy           =     0.3455
 eval_f1_macro           =     0.3154
 eval_loss               =     1.1391
 eval_precision          =      0.495
 eval_recall             =       0.32
 eval_runtime            = 0:00:05.66
 eval_samples_per_second =     97.116
 eval_steps_per_second   =      3.178

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0003
train_batch_size: 32
eval_batch_size: 32
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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1 Macro	Accuracy
No log	0	0	3.2995	0.0587	0.1667	0.0869	0.3524
0.7648	0.3368	1000	0.7304	0.4028	0.3358	0.3363	0.4918
0.7537	0.6736	2000	0.7005	0.4079	0.3483	0.3481	0.493
0.7174	1.0104	3000	0.6792	0.4157	0.3607	0.3625	0.5032
0.6713	1.3473	4000	0.6772	0.4212	0.3606	0.3630	0.484
0.6703	1.6841	5000	0.6570	0.4203	0.3585	0.3630	0.514
0.6936	2.0209	6000	0.6464	0.4116	0.3563	0.3603	0.5134
0.6942	2.3577	7000	0.6597	0.4005	0.3606	0.3627	0.5014
0.678	2.6945	8000	0.6517	0.4192	0.3652	0.3705	0.5244
0.6397	3.0313	9000	0.6397	0.4371	0.3669	0.3700	0.5126
0.6528	3.3681	10000	0.6725	0.4178	0.3699	0.3704	0.4856
0.6221	3.7050	11000	0.6370	0.4208	0.3672	0.3698	0.5108
0.5952	4.0418	12000	0.6464	0.4201	0.3629	0.3684	0.5248
0.614	4.3786	13000	0.6336	0.4247	0.3619	0.3667	0.5248
0.5978	4.7154	14000	0.6384	0.4205	0.3879	0.3903	0.5146
0.5992	5.0522	15000	0.6378	0.4238	0.3848	0.3886	0.516
0.61	5.3890	16000	0.6252	0.4302	0.3721	0.3764	0.5262
0.5936	5.7258	17000	0.6489	0.4754	0.4015	0.4092	0.517
0.5715	6.0626	18000	0.6327	0.4216	0.3769	0.3816	0.5168
0.5624	6.3995	19000	0.6425	0.4305	0.3812	0.3878	0.537
0.5979	6.7363	20000	0.6388	0.4243	0.3727	0.3759	0.5246
0.5284	7.0731	21000	0.6272	0.4234	0.3770	0.3814	0.5234
0.5926	7.4099	22000	0.6329	0.4978	0.3948	0.4108	0.531
0.5509	7.7467	23000	0.6361	0.5074	0.4001	0.4145	0.5198
0.5477	8.0835	24000	0.6281	0.4344	0.3776	0.3848	0.5284
0.5431	8.4203	25000	0.6586	0.4333	0.3568	0.3592	0.533
0.552	8.7572	26000	0.6311	0.5080	0.3937	0.4091	0.5242
0.5067	9.0940	27000	0.6317	0.4188	0.3794	0.3829	0.5194
0.5351	9.4308	28000	0.6339	0.4192	0.3782	0.3833	0.5254
0.5429	9.7676	29000	0.6277	0.4192	0.3811	0.3839	0.5226
0.5171	10.1044	30000	0.6314	0.5087	0.3889	0.4005	0.523
0.504	10.4412	31000	0.6608	0.4205	0.3807	0.3813	0.4998
0.5315	10.7780	32000	0.6389	0.4210	0.3767	0.3807	0.5198
0.5042	11.1149	33000	0.6375	0.4197	0.3795	0.3838	0.5258
0.5241	11.4517	34000	0.6423	0.4085	0.3783	0.3798	0.5168
0.5277	11.7885	35000	0.6428	0.4130	0.3795	0.3829	0.5282
0.505	12.1253	36000	0.6543	0.4182	0.3903	0.3905	0.5148
0.4923	12.4621	37000	0.6453	0.4181	0.3791	0.3832	0.5192
0.4689	12.7989	38000	0.6612	0.4317	0.4020	0.4042	0.5092
0.4658	13.1357	39000	0.6425	0.4131	0.3742	0.3781	0.522
0.4848	13.4725	40000	0.6549	0.4709	0.3844	0.3934	0.5064
0.4889	13.8094	41000	0.6459	0.4464	0.3893	0.3969	0.5198
0.4586	14.1462	42000	0.6515	0.4529	0.3927	0.4003	0.5218
0.4644	14.4830	43000	0.6429	0.4769	0.3825	0.3944	0.5258
0.4666	14.8198	44000	0.6565	0.4619	0.3963	0.4078	0.5182
0.4524	15.1566	45000	0.6487	0.4558	0.3882	0.3977	0.5222
0.4431	15.4934	46000	0.6475	0.4739	0.3851	0.3967	0.5266
0.4591	15.8302	47000	0.6521	0.4534	0.3862	0.3959	0.5244
0.4504	16.1671	48000	0.6543	0.4703	0.3817	0.3930	0.5234
0.4402	16.5039	49000	0.6601	0.4393	0.3975	0.4042	0.5132
0.4395	16.8407	50000	0.6556	0.4695	0.3849	0.3962	0.5238
0.4285	17.1775	51000	0.6577	0.4672	0.3852	0.3946	0.5178
0.4181	17.5143	52000	0.6540	0.4444	0.3854	0.3944	0.5226
0.4407	17.8511	53000	0.6544	0.4422	0.3868	0.3956	0.5262
0.3957	18.1879	54000	0.6594	0.4200	0.3868	0.3915	0.5182
0.4051	18.5248	55000	0.6572	0.4354	0.3861	0.3935	0.5192
0.424	18.8616	56000	0.6549	0.4476	0.3847	0.3941	0.5256
0.4271	19.1984	57000	0.6566	0.4471	0.3847	0.3936	0.5172
0.4225	19.5352	58000	0.6557	0.4473	0.3898	0.3984	0.5206
0.4312	19.8720	59000	0.6556	0.4468	0.3884	0.3973	0.5228

Framework versions

Transformers 4.53.2
Pytorch 2.7.1+cu126
Datasets 4.0.0
Tokenizers 0.21.2

versae/nb-sbert-base-edu-scorer-lr3e4-bs32

nb-sbert-base-edu-scorer-lr3e4-bs32

Model description

Intended uses & limitations

Test results

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

versae/
nb-sbert-base-edu-scorer-lr3e4-bs32