hing-mbert-ours-run-1

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: 2.9965
• Accuracy: 0.665
• Precision: 0.6151
• Recall: 0.6082
• F1: 0.6090

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
0.9757	1.0	100	0.7526	0.665	0.6358	0.6149	0.5854
0.7227	2.0	200	1.1062	0.69	0.6679	0.6031	0.6025
0.4345	3.0	300	1.2601	0.67	0.6512	0.6031	0.6001
0.2738	4.0	400	1.4485	0.67	0.6333	0.5968	0.6050
0.1171	5.0	500	1.9132	0.65	0.6216	0.6170	0.5944
0.0941	6.0	600	1.8293	0.685	0.6420	0.6439	0.6409
0.0348	7.0	700	2.3249	0.675	0.6424	0.6386	0.6384
0.0317	8.0	800	2.4134	0.67	0.6171	0.6120	0.6128
0.0056	9.0	900	2.6733	0.68	0.6343	0.6313	0.6300
0.0095	10.0	1000	2.5950	0.685	0.6318	0.6289	0.6295
0.0081	11.0	1100	2.3885	0.69	0.6407	0.6434	0.6410
0.023	12.0	1200	2.4087	0.67	0.6206	0.6231	0.6212
0.0054	13.0	1300	2.4516	0.675	0.6229	0.6227	0.6128
0.0047	14.0	1400	2.6152	0.68	0.6285	0.6256	0.6263
0.0063	15.0	1500	2.8077	0.69	0.6498	0.6281	0.6309
0.0028	16.0	1600	2.7084	0.675	0.6254	0.6214	0.6207
0.0025	17.0	1700	2.8360	0.67	0.6175	0.6128	0.6145
0.0011	18.0	1800	2.8591	0.655	0.6001	0.5958	0.5971
0.0005	19.0	1900	2.9419	0.665	0.6151	0.6082	0.6090
0.0002	20.0	2000	2.9965	0.665	0.6151	0.6082	0.6090

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.0+cu116
• Tokenizers 0.13.2