30-finetuned-spiderTraining50-200

This model is a fine-tuned version of facebook/convnextv2-tiny-22k-384 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.4873
• Accuracy: 0.8859
• Precision: 0.8884
• Recall: 0.8867
• F1: 0.8844

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: 0.0005
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• distributed_type: multi-GPU
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.5725	1.0	125	1.2861	0.6547	0.7027	0.6550	0.6374
1.1094	2.0	250	0.8928	0.7387	0.7725	0.7361	0.7306
1.153	3.0	375	0.9601	0.7117	0.7607	0.7069	0.7092
0.9492	4.0	500	0.9426	0.7107	0.7637	0.7084	0.7107
0.8308	5.0	625	0.8229	0.7608	0.7874	0.7525	0.7510
0.6969	6.0	750	0.8728	0.7658	0.7928	0.7620	0.7570
0.6008	7.0	875	0.7126	0.7968	0.8142	0.7936	0.7935
0.5553	8.0	1000	0.7980	0.7788	0.7986	0.7810	0.7746
0.6149	9.0	1125	0.8481	0.7908	0.8150	0.7983	0.7910
0.4931	10.0	1250	0.7269	0.8068	0.8216	0.8081	0.8015
0.4624	11.0	1375	0.7513	0.7978	0.8147	0.7952	0.7912
0.4795	12.0	1500	0.7173	0.8218	0.8362	0.8147	0.8178
0.4348	13.0	1625	0.6962	0.8158	0.8427	0.8179	0.8181
0.4129	14.0	1750	0.6100	0.8408	0.8426	0.8371	0.8347
0.3412	15.0	1875	0.7606	0.8148	0.8226	0.8142	0.8107
0.3238	16.0	2000	0.7354	0.8118	0.8305	0.8103	0.8079
0.2922	17.0	2125	0.7480	0.8228	0.8378	0.8250	0.8217
0.2478	18.0	2250	0.6308	0.8509	0.8613	0.8475	0.8472
0.2624	19.0	2375	0.6509	0.8338	0.8393	0.8328	0.8284
0.2183	20.0	2500	0.6546	0.8478	0.8568	0.8463	0.8454
0.2503	21.0	2625	0.6081	0.8549	0.8580	0.8541	0.8519
0.2578	22.0	2750	0.6065	0.8519	0.8546	0.8495	0.8469
0.2516	23.0	2875	0.5926	0.8629	0.8620	0.8603	0.8579
0.1922	24.0	3000	0.5702	0.8599	0.8626	0.8583	0.8545
0.1646	25.0	3125	0.5360	0.8779	0.8803	0.8770	0.8738
0.1595	26.0	3250	0.5625	0.8779	0.8814	0.8778	0.8747
0.1397	27.0	3375	0.5167	0.8889	0.8910	0.8887	0.8870
0.1323	28.0	3500	0.5151	0.8819	0.8850	0.8821	0.8796
0.1355	29.0	3625	0.4900	0.8899	0.8918	0.8904	0.8883
0.1673	30.0	3750	0.4873	0.8859	0.8884	0.8867	0.8844

Framework versions

• Transformers 4.33.3
• Pytorch 2.0.1+cu117
• Datasets 2.14.5
• Tokenizers 0.13.3