vakyansh-wav2vec2-punjabi-pam-10-audio-abuse-feature

This model is a fine-tuned version of Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.7070
• Accuracy: 0.7112
• Macro F1-score: 0.7112

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Macro F1-score
6.7326	0.77	10	6.7224	0.0	0.0
6.682	1.54	20	6.5714	0.2888	0.0298
6.523	2.31	30	6.3268	0.4877	0.3619
6.247	3.08	40	6.0039	0.4768	0.3229
6.0644	3.85	50	5.7134	0.4796	0.3241
5.7866	4.62	60	5.4332	0.4796	0.3241
5.5229	5.38	70	5.2026	0.4796	0.3241
5.2712	6.15	80	4.9856	0.4796	0.3241
5.1268	6.92	90	4.7918	0.4796	0.3241
4.9768	7.69	100	4.5999	0.4796	0.3241
4.7137	8.46	110	4.3958	0.4796	0.3241
4.5863	9.23	120	4.1988	0.4796	0.3241
4.3386	10.0	130	3.9983	0.4796	0.3241
4.1936	10.77	140	3.7938	0.4796	0.3241
3.9752	11.54	150	3.5906	0.4796	0.3241
3.9035	12.31	160	3.3854	0.4796	0.3241
3.652	13.08	170	3.1907	0.4796	0.3241
3.3045	13.85	180	2.9781	0.4796	0.3241
3.135	14.62	190	2.7764	0.4796	0.3241
2.9589	15.38	200	2.5827	0.4796	0.3241
2.7405	16.15	210	2.3901	0.4796	0.3241
2.5482	16.92	220	2.2042	0.4796	0.3241
2.4126	17.69	230	2.0318	0.4796	0.3241
2.2721	18.46	240	1.8672	0.4796	0.3241
2.0507	19.23	250	1.7156	0.4796	0.3241
1.8895	20.0	260	1.5721	0.4796	0.3241
1.7304	20.77	270	1.4453	0.4796	0.3241
1.5756	21.54	280	1.3330	0.4796	0.3241
1.4961	22.31	290	1.2238	0.6594	0.6321
1.4065	23.08	300	1.1468	0.6621	0.6356
1.4168	23.85	310	1.0636	0.6839	0.6632
1.1788	24.62	320	0.9818	0.7411	0.7325
1.06	25.38	330	0.9203	0.7466	0.7438
1.0021	26.15	340	0.8806	0.7629	0.7629
1.0249	26.92	350	0.8698	0.6894	0.6690
0.8521	27.69	360	0.7970	0.7602	0.7562
0.8504	28.46	370	0.7724	0.7602	0.7602
0.7939	29.23	380	0.7440	0.7466	0.7461
0.7805	30.0	390	0.7283	0.7520	0.7511
0.6974	30.77	400	0.7311	0.7384	0.7377
0.7533	31.54	410	0.7270	0.7112	0.6979
0.7528	32.31	420	0.6796	0.7357	0.7298
0.6679	33.08	430	0.6834	0.7357	0.7357
0.6732	33.85	440	0.6851	0.7248	0.7248
0.6001	34.62	450	0.6585	0.7548	0.7530
0.6731	35.38	460	0.6727	0.7411	0.7380
0.5601	36.15	470	0.6688	0.7330	0.7321
0.5488	36.92	480	0.6879	0.7439	0.7420
0.5892	37.69	490	0.6809	0.7166	0.7148
0.5651	38.46	500	0.6877	0.7193	0.7181
0.5595	39.23	510	0.6874	0.7221	0.7218
0.4983	40.0	520	0.6789	0.7166	0.7166
0.4869	40.77	530	0.6912	0.7221	0.7221
0.5352	41.54	540	0.7038	0.7003	0.6984
0.444	42.31	550	0.6778	0.7330	0.7321
0.5637	43.08	560	0.6873	0.6975	0.6973
0.5065	43.85	570	0.6736	0.7411	0.7405
0.4921	44.62	580	0.6859	0.7384	0.7381
0.4426	45.38	590	0.6995	0.7221	0.7221
0.4679	46.15	600	0.6967	0.7275	0.7271
0.6099	46.92	610	0.7145	0.6948	0.6947
0.4779	47.69	620	0.7026	0.7139	0.7139
0.4743	48.46	630	0.7036	0.7139	0.7137
0.4687	49.23	640	0.7060	0.7112	0.7112
0.459	50.0	650	0.7070	0.7112	0.7112

Framework versions

• Transformers 4.33.0
• Pytorch 2.0.0
• Datasets 2.1.0
• Tokenizers 0.13.3