hing-mbert-ours-run-4

This model is a fine-tuned version of l3cube-pune/hing-mbert on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 3.0173
• Accuracy: 0.68
• Precision: 0.6330
• Recall: 0.6325
• F1: 0.6320

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: 5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.9781	1.0	100	0.8852	0.55	0.4044	0.5284	0.4211
0.7568	2.0	200	0.8110	0.655	0.5994	0.6013	0.5762
0.5121	3.0	300	0.9735	0.65	0.6145	0.6131	0.5965
0.314	4.0	400	1.1324	0.65	0.6305	0.6355	0.6266
0.1298	5.0	500	2.8247	0.61	0.5804	0.5087	0.5092
0.1013	6.0	600	2.8183	0.635	0.6212	0.5674	0.5667
0.0989	7.0	700	2.3235	0.635	0.5944	0.5922	0.5916
0.0481	8.0	800	2.5240	0.68	0.6334	0.6172	0.6221
0.018	9.0	900	2.6782	0.65	0.6123	0.6054	0.6062
0.0285	10.0	1000	2.3400	0.67	0.6206	0.6327	0.6189
0.014	11.0	1100	2.6558	0.66	0.6098	0.5992	0.6018
0.0085	12.0	1200	2.9366	0.66	0.6076	0.5961	0.5991
0.0106	13.0	1300	2.8567	0.665	0.6198	0.6193	0.6186
0.0097	14.0	1400	3.1526	0.64	0.6089	0.5975	0.5954
0.0022	15.0	1500	2.7305	0.69	0.6404	0.6404	0.6398
0.0016	16.0	1600	2.7670	0.69	0.6418	0.6434	0.6425
0.0017	17.0	1700	2.8193	0.7	0.6533	0.6566	0.6546
0.0009	18.0	1800	2.9959	0.685	0.6400	0.6389	0.6384
0.0006	19.0	1900	3.0153	0.68	0.6330	0.6325	0.6320
0.0005	20.0	2000	3.0173	0.68	0.6330	0.6325	0.6320

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.0+cu116
• Tokenizers 0.13.2