hing-mbert-ours-run-3

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: 2.9769
• Accuracy: 0.675
• Precision: 0.6433
• Recall: 0.6344
• F1: 0.6344

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
0.9089	1.0	100	1.0993	0.635	0.6487	0.5304	0.5060
0.6657	2.0	200	0.8138	0.645	0.6550	0.6482	0.6234
0.3858	3.0	300	1.1334	0.665	0.6162	0.6061	0.5995
0.208	4.0	400	1.9041	0.685	0.6488	0.6169	0.6087
0.0996	5.0	500	2.3735	0.645	0.5867	0.5781	0.5794
0.0296	6.0	600	2.5772	0.665	0.6284	0.6208	0.6198
0.0623	7.0	700	2.8906	0.655	0.6040	0.5916	0.5926
0.0395	8.0	800	2.6567	0.675	0.6279	0.6254	0.6219
0.029	9.0	900	2.9277	0.655	0.6208	0.5950	0.5991
0.0194	10.0	1000	2.7362	0.665	0.6241	0.6208	0.6190
0.0092	11.0	1100	2.5561	0.68	0.6396	0.6401	0.6385
0.0059	12.0	1200	3.1112	0.675	0.6350	0.5967	0.6042
0.0133	13.0	1300	2.5269	0.685	0.6520	0.6607	0.6519
0.0051	14.0	1400	2.8736	0.68	0.6381	0.6158	0.6134
0.0044	15.0	1500	2.9132	0.675	0.6336	0.6180	0.6200
0.0029	16.0	1600	2.8701	0.675	0.6337	0.6214	0.6233
0.0015	17.0	1700	2.8115	0.69	0.6475	0.6388	0.6420
0.0019	18.0	1800	2.9517	0.67	0.6373	0.6276	0.6273
0.0013	19.0	1900	2.9633	0.67	0.6373	0.6276	0.6273
0.0007	20.0	2000	2.9769	0.675	0.6433	0.6344	0.6344

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.0+cu116
• Tokenizers 0.13.2