hubert_xlarge_emodb

This model is a fine-tuned version of facebook/hubert-xlarge-ll60k on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.8345
• Uar: 0.8889
• Acc: 0.9118

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: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Uar	Acc
No log	0.2	5	1.3815	0.25	0.1985
No log	0.39	10	1.3436	0.5285	0.5956
No log	0.59	15	1.3028	0.5741	0.6618
No log	0.78	20	1.2412	0.6019	0.6838
No log	0.98	25	1.1652	0.75	0.8015
1.2216	1.18	30	1.0883	0.7315	0.7868
1.2216	1.37	35	1.0309	0.75	0.8015
1.2216	1.57	40	1.0217	0.8335	0.8603
1.2216	1.76	45	1.0084	0.8714	0.8529
1.2216	1.96	50	0.9415	0.7778	0.8235
0.5781	2.16	55	0.9293	0.7870	0.8309
0.5781	2.35	60	0.8470	0.9448	0.9412
0.5781	2.55	65	0.8673	0.8333	0.8676
0.5781	2.75	70	0.8454	0.9074	0.9265
0.5781	2.94	75	0.8139	0.9167	0.9338
0.2652	3.14	80	0.8254	0.8981	0.9191
0.2652	3.33	85	0.8233	0.9074	0.9265
0.2652	3.53	90	0.7989	0.9259	0.9412
0.2652	3.73	95	0.7939	0.9584	0.9632
0.2652	3.92	100	0.8093	0.9167	0.9338
0.1537	4.12	105	0.8138	0.9167	0.9338
0.1537	4.31	110	0.7898	0.9539	0.9559
0.1537	4.51	115	0.8138	0.9074	0.9265
0.1537	4.71	120	0.8463	0.8704	0.8971
0.1537	4.9	125	0.8643	0.8519	0.8824
0.1615	5.1	130	0.8137	0.9074	0.9265
0.1615	5.29	135	0.7750	0.9724	0.9706
0.1615	5.49	140	0.7745	0.9724	0.9706
0.1615	5.69	145	0.8123	0.9074	0.9265
0.1615	5.88	150	0.8693	0.8426	0.875
0.0762	6.08	155	0.9067	0.7870	0.8309
0.0762	6.27	160	0.9123	0.7870	0.8309
0.0762	6.47	165	0.8664	0.8426	0.875
0.0762	6.67	170	0.8167	0.9074	0.9265
0.0762	6.86	175	0.8104	0.9259	0.9412
0.1321	7.06	180	0.8222	0.8981	0.9191
0.1321	7.25	185	0.8339	0.8889	0.9118
0.1321	7.45	190	0.8468	0.8704	0.8971
0.1321	7.65	195	0.8453	0.8704	0.8971
0.1321	7.84	200	0.8453	0.8704	0.8971
0.027	8.04	205	0.8346	0.8889	0.9118
0.027	8.24	210	0.8292	0.8889	0.9118
0.027	8.43	215	0.8276	0.8889	0.9118
0.027	8.63	220	0.8353	0.8889	0.9118
0.027	8.82	225	0.8376	0.8889	0.9118
0.0499	9.02	230	0.8327	0.8889	0.9118
0.0499	9.22	235	0.8317	0.8889	0.9118
0.0499	9.41	240	0.8330	0.8889	0.9118
0.0499	9.61	245	0.8343	0.8889	0.9118
0.0499	9.8	250	0.8345	0.8889	0.9118

Framework versions

• Transformers 4.32.0
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.13.3