intent_classify

This model is a fine-tuned version of facebook/wav2vec2-base-960h on the minds14 dataset. It achieves the following results on the evaluation set:

• Loss: 3.1513
• Accuracy: 0.0885

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: 3e-05
• train_batch_size: 64
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 200

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	8	2.6393	0.1239
2.6231	2.0	16	2.6391	0.1239
2.6382	3.0	24	2.6394	0.1239
2.6373	4.0	32	2.6409	0.0796
2.6421	5.0	40	2.6438	0.0619
2.6421	6.0	48	2.6447	0.0442
2.6388	7.0	56	2.6447	0.0265
2.6228	8.0	64	2.6457	0.0531
2.6305	9.0	72	2.6459	0.0442
2.6417	10.0	80	2.6500	0.0177
2.6417	11.0	88	2.6496	0.0354
2.6399	12.0	96	2.6453	0.0708
2.6355	13.0	104	2.6516	0.0354
2.652	14.0	112	2.6526	0.0796
2.6323	15.0	120	2.6592	0.0619
2.6323	16.0	128	2.6540	0.0619
2.6254	17.0	136	2.6519	0.0619
2.6377	18.0	144	2.6567	0.1150
2.6283	19.0	152	2.6649	0.0796
2.6192	20.0	160	2.6708	0.0796
2.6192	21.0	168	2.6700	0.0265
2.6289	22.0	176	2.6723	0.0177
2.6487	23.0	184	2.6700	0.0442
2.6324	24.0	192	2.6723	0.0265
2.6376	25.0	200	2.6639	0.0708
2.6376	26.0	208	2.6750	0.0177
2.6223	27.0	216	2.6837	0.0708
2.6323	28.0	224	2.6734	0.0796
2.6218	29.0	232	2.6737	0.0973
2.6231	30.0	240	2.6764	0.0708
2.6231	31.0	248	2.6793	0.0796
2.6228	32.0	256	2.6872	0.0442
2.6107	33.0	264	2.6816	0.0796
2.6078	34.0	272	2.6828	0.0796
2.6453	35.0	280	2.6746	0.0885
2.6453	36.0	288	2.6760	0.0885
2.6027	37.0	296	2.6885	0.0708
2.6179	38.0	304	2.6784	0.0973
2.5933	39.0	312	2.6808	0.0708
2.5909	40.0	320	2.6947	0.0796
2.5909	41.0	328	2.7123	0.0619
2.6179	42.0	336	2.7139	0.0619
2.5969	43.0	344	2.7160	0.0973
2.5966	44.0	352	2.7054	0.1062
2.6293	45.0	360	2.7260	0.0796
2.6293	46.0	368	2.6994	0.0973
2.6023	47.0	376	2.7039	0.0885
2.605	48.0	384	2.6680	0.0885
2.597	49.0	392	2.7001	0.0796
2.5936	50.0	400	2.7036	0.0796
2.5936	51.0	408	2.6866	0.0973
2.5823	52.0	416	2.7055	0.0973
2.5902	53.0	424	2.7130	0.0885
2.5793	54.0	432	2.7249	0.1062
2.5972	55.0	440	2.7253	0.1062
2.5972	56.0	448	2.6929	0.0885
2.5913	57.0	456	2.7252	0.0973
2.5937	58.0	464	2.7137	0.0796
2.5435	59.0	472	2.7252	0.0885
2.5475	60.0	480	2.7306	0.0708
2.5475	61.0	488	2.7158	0.0885
2.5591	62.0	496	2.7398	0.0531
2.6105	63.0	504	2.7323	0.0885
2.5593	64.0	512	2.7302	0.0885
2.574	65.0	520	2.7228	0.0973
2.574	66.0	528	2.7453	0.0885
2.549	67.0	536	2.7483	0.0796
2.543	68.0	544	2.7298	0.0531
2.5406	69.0	552	2.7341	0.0442
2.5245	70.0	560	2.7785	0.0708
2.5245	71.0	568	2.8005	0.0796
2.5764	72.0	576	2.7709	0.0708
2.529	73.0	584	2.7896	0.0796
2.5398	74.0	592	2.7806	0.0708
2.5436	75.0	600	2.7939	0.0796
2.5436	76.0	608	2.8015	0.0708
2.505	77.0	616	2.7643	0.0885
2.495	78.0	624	2.7971	0.0973
2.5473	79.0	632	2.8064	0.0796
2.5033	80.0	640	2.7837	0.0973
2.5033	81.0	648	2.7731	0.0885
2.5207	82.0	656	2.8325	0.0796
2.4956	83.0	664	2.7837	0.0885
2.494	84.0	672	2.8120	0.1150
2.4778	85.0	680	2.8099	0.0885
2.4778	86.0	688	2.7721	0.0885
2.4767	87.0	696	2.7981	0.1062
2.5287	88.0	704	2.8048	0.0531
2.4601	89.0	712	2.8174	0.0531
2.4073	90.0	720	2.8352	0.0708
2.4073	91.0	728	2.8099	0.0708
2.4156	92.0	736	2.8100	0.0973
2.4669	93.0	744	2.8282	0.0796
2.486	94.0	752	2.8443	0.0708
2.4439	95.0	760	2.8270	0.0796
2.4439	96.0	768	2.8432	0.0531
2.4477	97.0	776	2.8190	0.0885
2.4279	98.0	784	2.8173	0.0885
2.4116	99.0	792	2.8400	0.1150
2.3758	100.0	800	2.8620	0.0619
2.3758	101.0	808	2.8632	0.0442
2.4604	102.0	816	2.8518	0.0619
2.3987	103.0	824	2.8547	0.0354
2.3744	104.0	832	2.8221	0.0796
2.3797	105.0	840	2.8379	0.0531
2.3797	106.0	848	2.8498	0.0973
2.3711	107.0	856	2.8532	0.0796
2.3897	108.0	864	2.8078	0.1239
2.3108	109.0	872	2.8513	0.0885
2.2791	110.0	880	2.8794	0.1062
2.2791	111.0	888	2.8573	0.1062
2.3665	112.0	896	2.8454	0.1239
2.2993	113.0	904	2.8753	0.1062
2.3283	114.0	912	2.9077	0.1150
2.3286	115.0	920	2.9115	0.1150
2.3286	116.0	928	2.9227	0.0973
2.2489	117.0	936	2.9066	0.1062
2.2867	118.0	944	2.8912	0.0973
2.2914	119.0	952	2.9061	0.1239
2.2754	120.0	960	2.9024	0.1062
2.2754	121.0	968	2.9197	0.1062
2.2747	122.0	976	2.9232	0.0708
2.2423	123.0	984	2.9418	0.0973
2.2614	124.0	992	2.9615	0.0796
2.2761	125.0	1000	2.9344	0.1062
2.2761	126.0	1008	2.9412	0.0796
2.2377	127.0	1016	2.9950	0.1062
2.2496	128.0	1024	2.9659	0.1062
2.1857	129.0	1032	2.9819	0.1062
2.2464	130.0	1040	2.9840	0.1062
2.2464	131.0	1048	3.0030	0.0708
2.2035	132.0	1056	3.0148	0.0885
2.2497	133.0	1064	2.9930	0.0885
2.2877	134.0	1072	2.9949	0.0796
2.2939	135.0	1080	2.9780	0.0708
2.2939	136.0	1088	2.9763	0.0973
2.1982	137.0	1096	2.9887	0.0973
2.2871	138.0	1104	3.0152	0.0619
2.1358	139.0	1112	2.9990	0.0973
2.2026	140.0	1120	3.0051	0.0708
2.2026	141.0	1128	3.0082	0.0885
2.2605	142.0	1136	3.0037	0.0885
2.2208	143.0	1144	2.9893	0.0796
2.1251	144.0	1152	3.0048	0.0796
2.2273	145.0	1160	3.0431	0.0619
2.2273	146.0	1168	3.0515	0.0708
2.167	147.0	1176	3.0180	0.0708
2.0854	148.0	1184	3.0075	0.1062
2.1752	149.0	1192	3.0240	0.0973
2.0978	150.0	1200	3.0161	0.0885
2.0978	151.0	1208	3.0221	0.0885
2.2208	152.0	1216	3.0202	0.0796
2.0802	153.0	1224	3.0016	0.0708
2.1248	154.0	1232	3.0306	0.0796
2.0654	155.0	1240	3.0219	0.0885
2.0654	156.0	1248	3.0431	0.0885
2.0384	157.0	1256	2.9748	0.1062
2.24	158.0	1264	3.0428	0.1062
2.1095	159.0	1272	3.0469	0.0885
2.0334	160.0	1280	3.0766	0.0708
2.0334	161.0	1288	3.0622	0.0796
2.0276	162.0	1296	3.0810	0.0796
2.1454	163.0	1304	3.0658	0.0973
2.1115	164.0	1312	3.0959	0.0973
1.9748	165.0	1320	3.0532	0.1062
1.9748	166.0	1328	3.0925	0.0708
2.0136	167.0	1336	3.1021	0.0796
2.0998	168.0	1344	3.0993	0.0885
2.1276	169.0	1352	3.1212	0.0708
2.0504	170.0	1360	3.1114	0.0708
2.0504	171.0	1368	3.1104	0.0796
2.0975	172.0	1376	3.1190	0.0885
2.0216	173.0	1384	3.1361	0.0796
2.0501	174.0	1392	3.1312	0.0885
1.9737	175.0	1400	3.1344	0.0973
1.9737	176.0	1408	3.1300	0.0973
1.9742	177.0	1416	3.1306	0.0973
1.9977	178.0	1424	3.1490	0.0885
1.976	179.0	1432	3.1558	0.0973
2.0002	180.0	1440	3.1514	0.0973
2.0002	181.0	1448	3.1465	0.1062
2.004	182.0	1456	3.1450	0.1062
2.0281	183.0	1464	3.1352	0.1062
2.0258	184.0	1472	3.1503	0.1062
2.0263	185.0	1480	3.1394	0.0973
2.0263	186.0	1488	3.1274	0.1062
1.9759	187.0	1496	3.1319	0.1062
1.961	188.0	1504	3.1519	0.1062
2.0122	189.0	1512	3.1557	0.0973
2.0037	190.0	1520	3.1491	0.0885
2.0037	191.0	1528	3.1503	0.0885
1.9606	192.0	1536	3.1489	0.0885
1.9304	193.0	1544	3.1491	0.0885
2.0565	194.0	1552	3.1515	0.0973
2.0293	195.0	1560	3.1481	0.0885
2.0293	196.0	1568	3.1498	0.0885
2.0206	197.0	1576	3.1510	0.0885
1.9536	198.0	1584	3.1517	0.0885
2.0261	199.0	1592	3.1512	0.0885
2.1627	200.0	1600	3.1513	0.0885

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.2
• Datasets 2.12.0
• Tokenizers 0.13.2