hing-mbert-ours-run-5

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: 3.2437
• Accuracy: 0.665
• Precision: 0.6223
• Recall: 0.5991
• F1: 0.6039

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
0.9643	1.0	100	0.7996	0.69	0.6596	0.6593	0.6521
0.6951	2.0	200	1.0464	0.66	0.6597	0.5831	0.5734
0.4245	3.0	300	0.9640	0.64	0.6025	0.6033	0.6010
0.238	4.0	400	1.6744	0.68	0.7095	0.6445	0.6359
0.1477	5.0	500	1.7115	0.665	0.6362	0.6422	0.6360
0.1206	6.0	600	2.0459	0.635	0.5749	0.5752	0.5726
0.0528	7.0	700	2.5698	0.66	0.6230	0.5904	0.5985
0.0525	8.0	800	2.2729	0.625	0.5741	0.5860	0.5733
0.0174	9.0	900	2.6227	0.635	0.6099	0.6044	0.6019
0.0088	10.0	1000	2.8854	0.63	0.5699	0.5676	0.5680
0.0085	11.0	1100	3.2173	0.655	0.6043	0.5771	0.5821
0.0121	12.0	1200	3.1270	0.665	0.6214	0.5903	0.5971
0.0141	13.0	1300	2.6648	0.655	0.5981	0.5978	0.5961
0.0116	14.0	1400	3.1711	0.665	0.6192	0.5915	0.5971
0.007	15.0	1500	3.0954	0.66	0.6156	0.5961	0.6009
0.0037	16.0	1600	3.3065	0.65	0.6027	0.5791	0.5824
0.0031	17.0	1700	3.1715	0.665	0.6177	0.5999	0.6048
0.0021	18.0	1800	3.1602	0.665	0.6220	0.6029	0.6082
0.0021	19.0	1900	3.2027	0.655	0.6096	0.5893	0.5937
0.0018	20.0	2000	3.2437	0.665	0.6223	0.5991	0.6039

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.0+cu116
• Tokenizers 0.13.2