jialicheng/unlearn_speech_commands_hubert-base_bad_teaching_6_42

superb_ks_42

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

• Loss: 110529.0938
• Accuracy: 0.6171
• Test Accuracy: 0.6171
• Df Accuracy: 0.1272
• Unlearn Overall Accuracy: 0.7449
• Unlearn Time: 9120.1133

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: 5e-05
• train_batch_size: 32
• 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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Overall Accuracy	Unlearn Overall Accuracy	Time
0.0	0.5	1500	1.0208	0.6212	0.6347	0.6347	-1
0.0	1.0	3000	36.4693	0.2401	0.7025	0.7025	-1
0.0	1.5	4500	143.4027	0.1331	0.7435	0.7435	-1
0.0	2.0	6000	367.6124	0.1328	0.7402	0.7402	-1
0.0	2.5	7500	696.8393	0.1295	0.7370	0.7370	-1
0.0	3.0	9000	1265.9459	0.1312	0.7392	0.7392	-1
0.0	3.5	10500	2481.7976	0.1315	0.7389	0.7389	-1
0.0	4.0	12000	3998.9546	0.1318	0.7427	0.7427	-1
0.0	4.5	13500	5124.7690	0.1328	0.7415	0.7415	-1
0.0	5.0	15000	6240.3174	0.1325	0.7390	0.7390	-1
0.0	5.5	16500	65045.1367	0.1331	0.7439	0.7439	-1
0.0	6.0	18000	15838.1641	0.1325	0.7432	0.7432	-1
0.0	6.5	19500	11785.3887	0.1315	0.7418	0.7418	-1
0.0	7.0	21000	14938.7305	0.1305	0.7413	0.7413	-1
0.0	7.5	22500	156655.2344	0.1331	0.7439	0.7439	-1
0.0	7.99	24000	23587.6016	0.1308	0.7431	0.7431	-1
0.0	8.49	25500	21972.4258	0.1279	0.7375	0.7375	-1
0.0	8.99	27000	26555.4863	0.1315	0.7413	0.7413	-1
0.0	9.49	28500	31879.9805	0.1299	0.7430	0.7430	-1
0.0	9.99	30000	33662.6914	0.1285	0.7435	0.7435	-1
0.0	10.49	31500	44596.4219	0.1285	0.7448	0.7448	-1
0.0	10.99	33000	48498.2930	0.1269	0.7440	0.7440	-1
0.0	11.49	34500	58856.4648	0.1292	0.7448	0.7448	-1
0.0	11.99	36000	68769.0625	0.1295	0.7449	0.7449	-1
0.0	12.49	37500	65194.6914	0.1279	0.7442	0.7442	-1
0.0	12.99	39000	71167.4219	0.1299	0.7446	0.7446	-1
0.0	13.49	40500	77001.0547	0.1292	0.7439	0.7439	-1
0.0	13.99	42000	74238.8672	0.1292	0.7438	0.7438	-1
0.0	14.49	43500	76054.2656	0.1282	0.7443	0.7443	-1
0.0	14.99	45000	91555.7578	0.1279	0.7440	0.7440	-1
0.0	15.49	46500	96605.3203	0.1276	0.7448	0.7448	-1
0.0	15.99	48000	90379.8281	0.1279	0.7450	0.7450	-1
0.0	16.49	49500	91735.375	0.1279	0.7447	0.7447	-1
0.0	16.99	51000	103174.0234	0.1276	0.7452	0.7452	-1
0.0	17.49	52500	92643.7734	0.1279	0.7439	0.7439	-1
0.0	17.99	54000	105666.0156	0.1272	0.7449	0.7449	-1
0.0	18.49	55500	109315.1719	0.1276	0.7448	0.7448	-1
0.0	18.99	57000	117299.8359	0.1276	0.7450	0.7450	-1
0.0	19.49	58500	111527.3438	0.1272	0.7451	0.7451	-1
0.0	19.99	60000	110510.0156	0.1272	0.7449	0.7449	-1

Framework versions

• Transformers 4.39.3
• Pytorch 2.2.2+cu118
• Datasets 2.18.0
• Tokenizers 0.15.2