hing-mbert-ours-run-2

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: 3.3919
• Accuracy: 0.62
• Precision: 0.5759
• Recall: 0.5631
• F1: 0.5669

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
1.0284	1.0	100	1.2914	0.595	0.5712	0.4800	0.4642
0.8127	2.0	200	0.8552	0.59	0.5744	0.5675	0.4891
0.5499	3.0	300	1.1212	0.645	0.6544	0.5600	0.5475
0.3433	4.0	400	1.2017	0.605	0.5872	0.5866	0.5791
0.2218	5.0	500	1.8329	0.655	0.6458	0.6064	0.6055
0.1763	6.0	600	2.4194	0.655	0.6140	0.5802	0.5871
0.1022	7.0	700	2.3894	0.66	0.6171	0.6045	0.6048
0.0631	8.0	800	2.8259	0.605	0.5704	0.5255	0.5259
0.0254	9.0	900	2.9135	0.65	0.6013	0.5734	0.5784
0.0316	10.0	1000	3.0548	0.62	0.5862	0.5650	0.5670
0.026	11.0	1100	3.1020	0.62	0.5722	0.5593	0.5619
0.0152	12.0	1200	3.0692	0.62	0.5685	0.5597	0.5621
0.0156	13.0	1300	3.1068	0.615	0.5771	0.5589	0.5624
0.0237	14.0	1400	3.3487	0.62	0.5924	0.5589	0.5642
0.0094	15.0	1500	3.2007	0.615	0.5665	0.5639	0.5650
0.0054	16.0	1600	3.2838	0.62	0.5807	0.5657	0.5690
0.005	17.0	1700	3.2258	0.615	0.5846	0.5723	0.5747
0.005	18.0	1800	3.3572	0.63	0.5827	0.5698	0.5736
0.0022	19.0	1900	3.3642	0.62	0.5759	0.5631	0.5669
0.0019	20.0	2000	3.3919	0.62	0.5759	0.5631	0.5669

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.0+cu116
• Tokenizers 0.13.2