vakyansh-wav2vec2-kannada-knm-560-audio-abuse-feature

This model is a fine-tuned version of Harveenchadha/vakyansh-wav2vec2-kannada-knm-560 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.6403
• Accuracy: 0.7100
• Macro F1-score: 0.6596

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
6.6756	0.77	10	6.6487	0.0	0.0
6.6336	1.54	20	6.5448	0.5474	0.0647
6.4999	2.31	30	6.3245	0.6585	0.3971
6.2688	3.08	40	6.0120	0.6585	0.3971
6.0598	3.85	50	5.7401	0.6585	0.3971
5.7739	4.62	60	5.4859	0.6585	0.3971
5.5736	5.38	70	5.2443	0.6585	0.3971
5.3092	6.15	80	5.0361	0.6585	0.3971
5.1088	6.92	90	4.8282	0.6585	0.3971
4.9566	7.69	100	4.6295	0.6585	0.3971
4.7528	8.46	110	4.4350	0.6585	0.3971
4.6942	9.23	120	4.2479	0.6585	0.3971
4.4164	10.0	130	4.0578	0.6585	0.3971
4.1989	10.77	140	3.8571	0.6585	0.3971
4.0312	11.54	150	3.6581	0.6585	0.3971
3.8758	12.31	160	3.4561	0.6585	0.3971
3.7026	13.08	170	3.2569	0.6585	0.3971
3.4173	13.85	180	3.0592	0.6585	0.3971
3.2018	14.62	190	2.8633	0.6585	0.3971
3.1789	15.38	200	2.6746	0.6585	0.3971
2.8636	16.15	210	2.4860	0.6585	0.3971
2.6381	16.92	220	2.3059	0.6585	0.3971
2.5071	17.69	230	2.1303	0.6585	0.3971
2.2478	18.46	240	1.9669	0.6585	0.3971
2.2718	19.23	250	1.8162	0.6585	0.3971
2.0259	20.0	260	1.6750	0.6585	0.3971
1.8823	20.77	270	1.5460	0.6585	0.3971
1.6591	21.54	280	1.4290	0.6585	0.3971
1.5646	22.31	290	1.3213	0.6585	0.3971
1.487	23.08	300	1.2263	0.6585	0.3971
1.3681	23.85	310	1.1424	0.6585	0.3971
1.2941	24.62	320	1.0696	0.6585	0.3971
1.1374	25.38	330	1.0059	0.6585	0.3971
1.0881	26.15	340	0.9470	0.6585	0.3971
0.9892	26.92	350	0.8987	0.6585	0.3971
1.0156	27.69	360	0.8547	0.6585	0.3971
0.9592	28.46	370	0.8181	0.6585	0.3971
0.937	29.23	380	0.7861	0.6585	0.3971
0.8938	30.0	390	0.7572	0.6585	0.3971
0.8651	30.77	400	0.7331	0.6585	0.3971
0.8051	31.54	410	0.7182	0.6585	0.3971
0.7774	32.31	420	0.7072	0.6585	0.3971
0.749	33.08	430	0.6787	0.6585	0.3971
0.7762	33.85	440	0.6687	0.6585	0.3971
0.7223	34.62	450	0.6656	0.7480	0.6544
0.7363	35.38	460	0.6619	0.7534	0.6963
0.7039	36.15	470	0.6473	0.7371	0.6867
0.6923	36.92	480	0.6377	0.7453	0.6854
0.6667	37.69	490	0.6405	0.7317	0.6786
0.6419	38.46	500	0.6479	0.7127	0.6794
0.6511	39.23	510	0.6336	0.7344	0.6757
0.6638	40.0	520	0.6244	0.7236	0.6927
0.67	40.77	530	0.6241	0.7290	0.6795
0.616	41.54	540	0.6353	0.7182	0.6789
0.6592	42.31	550	0.6277	0.7344	0.6890
0.6146	43.08	560	0.6352	0.7236	0.6890
0.6103	43.85	570	0.6382	0.7100	0.6629
0.6099	44.62	580	0.6373	0.7100	0.6629
0.5724	45.38	590	0.6358	0.7182	0.6667
0.6134	46.15	600	0.6410	0.7073	0.6680
0.6084	46.92	610	0.6441	0.7127	0.6755
0.656	47.69	620	0.6400	0.7127	0.6727
0.6359	48.46	630	0.6405	0.7100	0.6689
0.5832	49.23	640	0.6407	0.7073	0.6621
0.5822	50.0	650	0.6403	0.7100	0.6596

Framework versions

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