metadata

license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: hubert-classifier-aug
    results: []

hubert-classifier-aug

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

Loss: 0.5817
Accuracy: 0.8356
Precision: 0.8647
Recall: 0.8356
F1: 0.8286
Binary: 0.8852

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: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Binary
No log	0.19	50	4.0265	0.0404	0.0071	0.0404	0.0079	0.3011
No log	0.38	100	3.5428	0.0485	0.0048	0.0485	0.0080	0.3286
No log	0.58	150	3.3405	0.0836	0.0175	0.0836	0.0251	0.3555
No log	0.77	200	3.2238	0.0809	0.0143	0.0809	0.0216	0.3512
No log	0.96	250	3.1041	0.0728	0.0135	0.0728	0.0202	0.3493
No log	1.15	300	2.9851	0.1078	0.0517	0.1078	0.0480	0.3730
No log	1.34	350	2.8525	0.1779	0.0780	0.1779	0.0876	0.4197
No log	1.53	400	2.7647	0.1752	0.1108	0.1752	0.1064	0.4221
No log	1.73	450	2.5521	0.2291	0.1539	0.2291	0.1450	0.4547
3.3693	1.92	500	2.4121	0.2372	0.1655	0.2372	0.1618	0.4668
3.3693	2.11	550	2.2312	0.2992	0.2286	0.2992	0.2185	0.5081
3.3693	2.3	600	2.0065	0.4124	0.2985	0.4124	0.3133	0.5865
3.3693	2.49	650	1.8816	0.4313	0.3359	0.4313	0.3461	0.6013
3.3693	2.68	700	1.8069	0.4906	0.4702	0.4906	0.4308	0.6426
3.3693	2.88	750	1.6310	0.5418	0.4981	0.5418	0.4728	0.6803
3.3693	3.07	800	1.5274	0.5580	0.5219	0.5580	0.5002	0.6908
3.3693	3.26	850	1.3417	0.6415	0.6343	0.6415	0.5980	0.7544
3.3693	3.45	900	1.3121	0.6173	0.6059	0.6173	0.5690	0.7334
3.3693	3.64	950	1.2298	0.6523	0.6501	0.6523	0.6183	0.7577
2.2303	3.84	1000	1.1427	0.7197	0.7323	0.7197	0.6897	0.8040
2.2303	4.03	1050	1.0947	0.6765	0.6891	0.6765	0.6387	0.7741
2.2303	4.22	1100	1.1233	0.6361	0.6473	0.6361	0.6054	0.7447
2.2303	4.41	1150	0.9765	0.7547	0.7606	0.7547	0.7331	0.8296
2.2303	4.6	1200	0.9206	0.7547	0.7546	0.7547	0.7270	0.8305
2.2303	4.79	1250	0.8658	0.7790	0.7868	0.7790	0.7625	0.8456
2.2303	4.99	1300	0.8961	0.7385	0.7576	0.7385	0.7254	0.8186
2.2303	5.18	1350	0.7709	0.8005	0.8185	0.8005	0.7912	0.8596
2.2303	5.37	1400	0.7638	0.7925	0.8118	0.7925	0.7760	0.8547
2.2303	5.56	1450	0.7085	0.8194	0.8415	0.8194	0.8081	0.8741
1.6078	5.75	1500	0.7230	0.7790	0.8195	0.7790	0.7739	0.8456
1.6078	5.94	1550	0.6475	0.7951	0.8174	0.7951	0.7813	0.8558
1.6078	6.14	1600	0.6910	0.7844	0.8082	0.7844	0.7686	0.8504
1.6078	6.33	1650	0.6233	0.8194	0.8462	0.8194	0.8111	0.8730
1.6078	6.52	1700	0.6599	0.8059	0.8429	0.8059	0.8031	0.8633
1.6078	6.71	1750	0.6999	0.7925	0.8119	0.7925	0.7751	0.8550
1.6078	6.9	1800	0.6271	0.8140	0.8266	0.8140	0.8018	0.8701
1.6078	7.09	1850	0.5545	0.8329	0.8557	0.8329	0.8288	0.8822
1.6078	7.29	1900	0.6343	0.8032	0.8179	0.8032	0.7930	0.8625
1.6078	7.48	1950	0.6007	0.8194	0.8447	0.8194	0.8136	0.8728
1.2974	7.67	2000	0.5878	0.8356	0.8674	0.8356	0.8333	0.8841
1.2974	7.86	2050	0.6410	0.8086	0.8344	0.8086	0.8011	0.8652
1.2974	8.05	2100	0.6430	0.8005	0.8201	0.8005	0.7894	0.8598
1.2974	8.25	2150	0.5540	0.8221	0.8414	0.8221	0.8177	0.8747
1.2974	8.44	2200	0.5511	0.8356	0.8635	0.8356	0.8317	0.8833
1.2974	8.63	2250	0.5817	0.8356	0.8647	0.8356	0.8286	0.8852

Framework versions

Transformers 4.38.2
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.15.1