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nzm97/math_question_grade_detection_Bert_databalanced_v2

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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mathquestiongradedetectionBertdatabalancedv2

This model is a fine-tuned version of google-bert/bert-large-uncased on an unknown dataset. It achieves the following results on the evaluation set:

—Loss: 0.5945
—Accuracy: 0.8127
—Precision: 0.8116
—Recall: 0.8127
—F1: 0.8110

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
—trainbatchsize: 16
—evalbatchsize: 8
—seed: 42
—gradientaccumulationsteps: 2
—totaltrainbatch_size: 32
—optimizer: Use adamwtorch with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
—lrschedulertype: linear
—lrschedulerwarmup_steps: 200
—training_steps: 1000
—mixedprecisiontraining: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	0.2817	50	2.1406	0.1698	0.1183	0.1698	0.1327
No log	0.5634	100	1.8833	0.3540	0.3387	0.3540	0.2911
No log	0.8451	150	1.5465	0.4365	0.4580	0.4365	0.4060
No log	1.1268	200	1.2969	0.4937	0.4950	0.4937	0.4471
No log	1.4085	250	1.0146	0.6143	0.6253	0.6143	0.5906
No log	1.6901	300	0.8713	0.6778	0.6771	0.6778	0.6476
No log	1.9718	350	0.7740	0.7016	0.7000	0.7016	0.6896
No log	2.2535	400	0.7760	0.6968	0.7068	0.6968	0.6872
No log	2.5352	450	0.6579	0.7619	0.7726	0.7619	0.7590
1.2792	2.8169	500	0.6872	0.7429	0.7571	0.7429	0.7418
1.2792	3.0986	550	0.6073	0.7698	0.7783	0.7698	0.7700
1.2792	3.3803	600	0.6297	0.7714	0.7840	0.7714	0.7718
1.2792	3.6620	650	0.6160	0.7762	0.7764	0.7762	0.7731
1.2792	3.9437	700	0.5895	0.8111	0.8147	0.8111	0.8110
1.2792	4.2254	750	0.5717	0.8111	0.8087	0.8111	0.8089
1.2792	4.5070	800	0.5767	0.8095	0.8126	0.8095	0.8083
1.2792	4.7887	850	0.5898	0.8016	0.8029	0.8016	0.7995
1.2792	5.0704	900	0.5908	0.8127	0.8143	0.8127	0.8115
1.2792	5.3521	950	0.5972	0.8111	0.8136	0.8111	0.8102
0.304	5.6338	1000	0.5945	0.8127	0.8116	0.8127	0.8110

Framework versions

—Transformers 4.46.3
—Pytorch 2.4.0
—Datasets 3.1.0
—Tokenizers 0.20.3