hubert-base-ser

RamiEbeid/hubert-base-ser

hubert-base-ser is a machine learning model from RamiEbeid. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
License: apache-2.0
Model type: hubert
Access: Public
Created: Mar 10, 2022
Updated: Mar 16, 2022
SHA: 8127ef59

Model card

From the Hugging Face model README

hubert-base-ser

This model is a fine-tuned version of facebook/hubert-base-ls960 on the Crema dataset. It achieves the following results on the evaluation set:

Loss: 1.0105
Accuracy: 0.6313

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: 0.0001
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.8106	0.01	10	1.7616	0.1974
1.7268	0.03	20	1.7187	0.2525
1.7269	0.04	30	1.6442	0.3096
1.7086	0.05	40	1.5834	0.3338
1.6983	0.07	50	1.6195	0.3600
1.5845	0.08	60	1.5753	0.3418
1.5744	0.09	70	1.5669	0.3707
1.5915	0.11	80	1.5412	0.3754
1.5105	0.12	90	2.0037	0.2612
1.4689	0.13	100	1.5440	0.3627
1.527	0.15	110	1.5400	0.3862
1.6481	0.16	120	1.6678	0.3298
1.7504	0.17	130	1.6078	0.2995
1.3748	0.19	140	1.5750	0.3251
1.6417	0.2	150	1.7034	0.2599
1.6146	0.21	160	1.6162	0.3519
1.4896	0.23	170	1.5245	0.3741
1.4278	0.24	180	1.7537	0.2424
1.4475	0.26	190	1.4769	0.3882
1.5416	0.27	200	1.4772	0.3949
1.5997	0.28	210	1.4428	0.4278
1.4337	0.3	220	1.4352	0.4124
1.415	0.31	230	1.4405	0.4157
1.5196	0.32	240	1.4197	0.4043
1.3866	0.34	250	1.5241	0.3734
1.3041	0.35	260	1.5703	0.4043
1.3618	0.36	270	1.3963	0.4285
1.3293	0.38	280	1.3478	0.4506
1.2215	0.39	290	1.5994	0.3842
1.6618	0.4	300	1.7751	0.2277
1.5349	0.42	310	1.6091	0.4036
1.4037	0.43	320	1.4741	0.4446
1.4844	0.44	330	1.4170	0.4399
1.2806	0.46	340	1.2887	0.5050
1.3818	0.47	350	1.2668	0.5017
1.3491	0.48	360	1.4721	0.4594
1.2347	0.5	370	1.2188	0.5245
1.2182	0.51	380	1.3813	0.4567
1.2513	0.52	390	1.2111	0.5205
1.2447	0.54	400	1.2231	0.5460
1.038	0.55	410	1.2563	0.5373
1.2409	0.56	420	1.3448	0.4936
1.2279	0.58	430	1.1972	0.5487
1.3256	0.59	440	1.1706	0.5742
1.2866	0.6	450	1.3091	0.5003
1.0574	0.62	460	1.2075	0.5500
1.2744	0.63	470	1.2831	0.5171
1.0836	0.64	480	1.1768	0.5608
1.135	0.66	490	1.1408	0.5776
1.1303	0.67	500	1.2320	0.5541
1.2068	0.69	510	1.1379	0.5796
1.1347	0.7	520	1.1124	0.5897
1.1846	0.71	530	1.1338	0.5803
1.2409	0.73	540	1.1259	0.5789
1.0664	0.74	550	1.0653	0.6038
1.1637	0.75	560	1.0550	0.5977
1.0707	0.77	570	1.0996	0.5715
1.2258	0.78	580	1.0804	0.5977
0.9256	0.79	590	1.1501	0.5809
1.1542	0.81	600	1.1089	0.5957
1.3931	0.82	610	1.1381	0.5856
1.1117	0.83	620	1.0933	0.6031
1.1433	0.85	630	1.0175	0.6219
1.0325	0.86	640	0.9885	0.6239
1.111	0.87	650	1.0048	0.6259
0.8125	0.89	660	1.0176	0.6165
1.0414	0.9	670	1.0290	0.6185
1.0037	0.91	680	1.0269	0.6253
0.9406	0.93	690	1.0301	0.6273
1.0129	0.94	700	1.0238	0.6326
1.2213	0.95	710	1.0181	0.6273
1.2519	0.97	720	1.0161	0.6266
0.9932	0.98	730	1.0112	0.6279
1.0135	0.99	740	1.0105	0.6313

Framework versions

Transformers 4.18.0.dev0
Pytorch 1.10.0+cu111
Datasets 1.18.5.dev0
Tokenizers 0.11.6

Library: transformers
Type: hubert
License: apache-2.0
Created: Mar 10, 2022
Updated: Mar 16, 2022