Validation Report:
              precision    recall  f1-score   support

           0       0.78      0.40      0.53       100
           1       0.32      0.48      0.39       100
           2       0.20      0.43      0.28       100
           3       0.26      0.28      0.27       100
           4       0.43      0.12      0.19       100
           5       0.83      0.10      0.18        50

    accuracy                           0.32       550
   macro avg       0.47      0.30      0.31       550
weighted avg       0.44      0.32      0.32       550

Confusion Matrix:

[[40 49  9  2  0  0]
 [ 8 48 39  5  0  0]
 [ 3 40 43 14  0  0]
 [ 0 11 57 28  3  1]
 [ 0  1 48 39 12  0]
 [ 0  0 14 18 13  5]]

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: 32
eval_batch_size: 32
seed: 0
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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1 Macro	Accuracy
No log	0	0	3.1303	0.0587	0.1667	0.0869	0.3524
1.3347	0.3368	1000	1.3421	0.2177	0.1956	0.1184	0.2888
1.0654	0.6736	2000	1.0316	0.3168	0.2483	0.1970	0.3602
0.9114	1.0104	3000	0.8773	0.4229	0.2815	0.2568	0.4066
0.8105	1.3473	4000	0.8171	0.4066	0.3068	0.2945	0.4512
0.7636	1.6841	5000	0.7893	0.3960	0.3157	0.3084	0.461
0.7694	2.0209	6000	0.7896	0.4062	0.3146	0.3069	0.478
0.8137	2.3577	7000	0.7668	0.4041	0.3376	0.3353	0.4686
0.7579	2.6945	8000	0.7561	0.3984	0.3334	0.3321	0.469
0.7641	3.0313	9000	0.7481	0.3952	0.3312	0.3283	0.473
0.7346	3.3681	10000	0.7433	0.4059	0.3306	0.3287	0.4756
0.764	3.7050	11000	0.7404	0.4114	0.3358	0.3321	0.481
0.7794	4.0418	12000	0.7360	0.3937	0.3324	0.3282	0.4728
0.7563	4.3786	13000	0.7456	0.3868	0.3303	0.3268	0.4576
0.7549	4.7154	14000	0.7349	0.3937	0.3329	0.3300	0.4674
0.7449	5.0522	15000	0.7314	0.3930	0.3346	0.3310	0.4692
0.7486	5.3890	16000	0.7307	0.3902	0.3341	0.3312	0.4742
0.718	5.7258	17000	0.7405	0.3968	0.3306	0.3263	0.4942
0.729	6.0626	18000	0.7275	0.4005	0.3347	0.3330	0.4832
0.7594	6.3995	19000	0.7321	0.3952	0.3383	0.3361	0.4944
0.7221	6.7363	20000	0.7278	0.3921	0.3390	0.3367	0.4798
0.7219	7.0731	21000	0.7275	0.3968	0.3373	0.3356	0.4908
0.7216	7.4099	22000	0.7396	0.3902	0.3427	0.3388	0.4686
0.7388	7.7467	23000	0.7346	0.3886	0.3414	0.3368	0.471
0.7098	8.0835	24000	0.7216	0.3916	0.3398	0.3372	0.4892
0.7207	8.4203	25000	0.7218	0.3962	0.3413	0.3397	0.4906
0.7609	8.7572	26000	0.7226	0.3949	0.3365	0.3348	0.4894
0.7791	9.0940	27000	0.7199	0.3943	0.3387	0.3357	0.483
0.7499	9.4308	28000	0.7204	0.3952	0.3404	0.3371	0.4844
0.7025	9.7676	29000	0.7212	0.3932	0.3390	0.3372	0.4956
0.7157	10.1044	30000	0.7177	0.3941	0.3394	0.3379	0.4934
0.7214	10.4412	31000	0.7202	0.3931	0.3411	0.3394	0.4964
0.702	10.7780	32000	0.7167	0.3942	0.3412	0.3386	0.491
0.7421	11.1149	33000	0.7204	0.3873	0.3396	0.3366	0.4776
0.7127	11.4517	34000	0.7167	0.3978	0.3384	0.3368	0.4892
0.7262	11.7885	35000	0.7178	0.3912	0.3392	0.3384	0.486
0.6968	12.1253	36000	0.7156	0.3853	0.3400	0.3362	0.4886
0.7083	12.4621	37000	0.7180	0.3880	0.3429	0.3397	0.4838
0.7504	12.7989	38000	0.7167	0.3872	0.3399	0.3378	0.4914
0.6926	13.1357	39000	0.7143	0.3955	0.3406	0.3381	0.4922
0.7415	13.4725	40000	0.7258	0.3912	0.3445	0.3404	0.4762
0.7099	13.8094	41000	0.7171	0.3904	0.3417	0.3401	0.4962
0.7185	14.1462	42000	0.7146	0.3909	0.3382	0.3359	0.4864
0.7064	14.4830	43000	0.7158	0.3908	0.3437	0.3418	0.4876
0.6991	14.8198	44000	0.7128	0.3955	0.3397	0.3382	0.4932
0.7532	15.1566	45000	0.7149	0.3907	0.3421	0.3403	0.4866
0.7382	15.4934	46000	0.7141	0.3931	0.3424	0.3414	0.4908
0.7254	15.8302	47000	0.7119	0.3913	0.3402	0.3382	0.4914
0.7358	16.1671	48000	0.7166	0.3899	0.3442	0.3416	0.4892
0.7439	16.5039	49000	0.7140	0.3892	0.3426	0.3405	0.4864
0.692	16.8407	50000	0.7121	0.3904	0.3374	0.3364	0.491
0.7439	17.1775	51000	0.7130	0.3929	0.3440	0.3419	0.4886
0.7105	17.5143	52000	0.7127	0.3858	0.3382	0.3359	0.4902
0.6938	17.8511	53000	0.7121	0.3872	0.3389	0.3361	0.4884
0.6654	18.1879	54000	0.7129	0.3908	0.3419	0.3403	0.4856
0.7289	18.5248	55000	0.7117	0.3914	0.3416	0.3396	0.4898
0.7059	18.8616	56000	0.7122	0.3886	0.3395	0.3374	0.4932
0.7316	19.1984	57000	0.7112	0.3874	0.3399	0.3372	0.4902
0.7465	19.5352	58000	0.7111	0.3885	0.3418	0.3390	0.492
0.7108	19.8720	59000	0.7114	0.3932	0.3445	0.3426	0.4914

Framework versions

Transformers 4.53.2
Pytorch 2.7.1+cu126
Datasets 4.0.0
Tokenizers 0.21.2

versae/nb-bert-edu-scorer-lr2e5-bs32

nb-bert-edu-scorer-lr2e5-bs32

Model description

Intended uses & limitations

Test results

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

versae/
nb-bert-edu-scorer-lr2e5-bs32