math_question_grade_detection_Bert_databalanced

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: 0.6880
• Accuracy: 0.7603
• Precision: 0.7651
• Recall: 0.7603
• F1: 0.7588

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: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Use 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_steps: 100
• training_steps: 1100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	0.2817	50	2.1003	0.2349	0.3799	0.2349	0.2106
No log	0.5634	100	1.9607	0.2762	0.3337	0.2762	0.2498
No log	0.8451	150	1.5031	0.4778	0.4633	0.4778	0.4591
No log	1.1268	200	1.2546	0.5460	0.5596	0.5460	0.5176
No log	1.4085	250	1.0941	0.5746	0.5804	0.5746	0.5675
No log	1.6901	300	0.9381	0.6730	0.6943	0.6730	0.6721
No log	1.9718	350	0.8974	0.6619	0.6822	0.6619	0.6570
No log	2.2535	400	0.8243	0.6889	0.6913	0.6889	0.6856
No log	2.5352	450	0.8219	0.6937	0.7131	0.6937	0.6881
1.2537	2.8169	500	0.7642	0.7159	0.7239	0.7159	0.7121
1.2537	3.0986	550	0.7580	0.7175	0.7197	0.7175	0.7068
1.2537	3.3803	600	0.7310	0.7397	0.7523	0.7397	0.7387
1.2537	3.6620	650	0.7562	0.7413	0.7466	0.7413	0.7349
1.2537	3.9437	700	0.6512	0.7730	0.7792	0.7730	0.7726
1.2537	4.2254	750	0.6941	0.7476	0.7484	0.7476	0.7447
1.2537	4.5070	800	0.6866	0.7571	0.7607	0.7571	0.7550
1.2537	4.7887	850	0.6942	0.7603	0.7644	0.7603	0.7588
1.2537	5.0704	900	0.7230	0.7683	0.7821	0.7683	0.7656
1.2537	5.3521	950	0.7123	0.7603	0.7669	0.7603	0.7588
0.321	5.6338	1000	0.6939	0.7667	0.7725	0.7667	0.7652
0.321	5.9155	1050	0.6884	0.7667	0.7723	0.7667	0.7657
0.321	6.1972	1100	0.6880	0.7603	0.7651	0.7603	0.7588

Framework versions

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