learning_rate: 3e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	2	3.8343	0.0794
No log	2.0	4	3.7130	0.0952
No log	3.0	6	3.5158	0.1111
No log	4.0	8	3.2104	0.1111
No log	5.0	10	2.6008	0.1111
No log	6.0	12	1.5964	0.5714
No log	7.0	14	1.3373	0.5079
No log	8.0	16	1.2256	0.5079
No log	9.0	18	0.9717	0.5079
2.4295	10.0	20	0.7747	0.5397
2.4295	11.0	22	0.6721	0.5556
2.4295	12.0	24	0.5051	0.8571
2.4295	13.0	26	0.3946	0.9206
2.4295	14.0	28	0.3315	0.9365
2.4295	15.0	30	0.3101	0.9524
2.4295	16.0	32	0.2873	0.9524
2.4295	17.0	34	0.2621	0.9683
2.4295	18.0	36	0.2666	0.9365
2.4295	19.0	38	0.2871	0.9365
0.3966	20.0	40	0.2009	0.9683
0.3966	21.0	42	0.3878	0.9048
0.3966	22.0	44	0.1284	0.9841
0.3966	23.0	46	0.1758	0.9683
0.3966	24.0	48	0.1843	0.9683
0.3966	25.0	50	0.1165	0.9841
0.3966	26.0	52	0.1204	0.9841
0.3966	27.0	54	0.1801	0.9683
0.3966	28.0	56	0.4004	0.9048
0.3966	29.0	58	0.3515	0.9206
0.1757	30.0	60	0.4325	0.9048
0.1757	31.0	62	0.1757	0.9683
0.1757	32.0	64	0.3189	0.8889
0.1757	33.0	66	0.3106	0.9048
0.1757	34.0	68	0.2002	0.9524
0.1757	35.0	70	0.1737	0.9683
0.1757	36.0	72	0.1659	0.9683
0.1757	37.0	74	0.1682	0.9683
0.1757	38.0	76	0.2712	0.9365
0.1757	39.0	78	0.3072	0.9206
0.114	40.0	80	0.2328	0.9524
0.114	41.0	82	0.1956	0.9524
0.114	42.0	84	0.1763	0.9683
0.114	43.0	86	0.1677	0.9683
0.114	44.0	88	0.1669	0.9683
0.114	45.0	90	0.2476	0.9365
0.114	46.0	92	0.2978	0.9206
0.114	47.0	94	0.1630	0.9683
0.114	48.0	96	0.1587	0.9683
0.114	49.0	98	0.1570	0.9683
0.0618	50.0	100	0.1557	0.9683
0.0618	51.0	102	0.2235	0.9524
0.0618	52.0	104	0.2377	0.9365
0.0618	53.0	106	0.2038	0.9524
0.0618	54.0	108	0.2093	0.9524
0.0618	55.0	110	0.2572	0.9365
0.0618	56.0	112	0.3067	0.9365
0.0618	57.0	114	0.2866	0.9206
0.0618	58.0	116	0.2155	0.9524
0.0618	59.0	118	0.2142	0.9524
0.0581	60.0	120	0.2086	0.9524
0.0581	61.0	122	0.1915	0.9524
0.0581	62.0	124	0.1775	0.9524
0.0581	63.0	126	0.1962	0.9524
0.0581	64.0	128	0.2140	0.9524
0.0581	65.0	130	0.2255	0.9524
0.0581	66.0	132	0.2303	0.9524
0.0581	67.0	134	0.2321	0.9524
0.0581	68.0	136	0.2311	0.9524
0.0581	69.0	138	0.2265	0.9524
0.0293	70.0	140	0.2208	0.9524
0.0293	71.0	142	0.2155	0.9524
0.0293	72.0	144	0.2121	0.9524
0.0293	73.0	146	0.2104	0.9524
0.0293	74.0	148	0.2093	0.9524
0.0293	75.0	150	0.2032	0.9524
0.0293	76.0	152	0.1900	0.9524
0.0293	77.0	154	0.1778	0.9683
0.0293	78.0	156	0.1753	0.9683
0.0293	79.0	158	0.1748	0.9683
0.0197	80.0	160	0.1746	0.9683
0.0197	81.0	162	0.1780	0.9683
0.0197	82.0	164	0.1970	0.9524
0.0197	83.0	166	0.2126	0.9524
0.0197	84.0	168	0.2201	0.9524
0.0197	85.0	170	0.2239	0.9524
0.0197	86.0	172	0.2264	0.9524
0.0197	87.0	174	0.2279	0.9524
0.0197	88.0	176	0.2291	0.9524
0.0197	89.0	178	0.2299	0.9524
0.0153	90.0	180	0.2306	0.9524
0.0153	91.0	182	0.2312	0.9524
0.0153	92.0	184	0.2318	0.9524
0.0153	93.0	186	0.2323	0.9524
0.0153	94.0	188	0.2327	0.9524
0.0153	95.0	190	0.2331	0.9524
0.0153	96.0	192	0.2333	0.9524
0.0153	97.0	194	0.2335	0.9524
0.0153	98.0	196	0.2336	0.9524
0.0153	99.0	198	0.2338	0.9524
0.0142	100.0	200	0.2339	0.9524

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

Hakeem750/pet_audio_class

pet_audio_class

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Hakeem750/
pet_audio_class