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Bingsu/
distilhubert-ko-zeroth

Name: distilhubert-ko-zeroth
Availability: InStock

transformers

pytorch

hubert

automatic-speech-recognition

zeroth

generated_from_trainer

apache-2.0

endpoints_compatible

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distilhubert-ko-zeroth

This model is a fine-tuned version of ntu-spml/distilhubert on the BINGSU/ZEROTH-KOREAN - NA dataset. It achieves the following results on the evaluation set:

Loss: 0.9934
Cer: 0.2066

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: 0.001
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine_with_restarts
lr_scheduler_warmup_steps: 500
num_epochs: 10.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
No log	0.57	400	3.2681	0.6761
7.285	1.15	800	1.5312	0.4170
1.259	1.72	1200	1.3459	0.3846
0.9108	2.3	1600	1.1357	0.3239
0.7227	2.87	2000	1.0571	0.3056
0.7227	3.45	2400	1.0002	0.2829
0.5689	4.02	2800	0.8773	0.2553
0.4676	4.6	3200	0.8634	0.2462
0.3805	5.17	3600	0.8504	0.2323
0.2548	5.75	4000	0.8480	0.2260
0.2548	6.32	4400	0.8550	0.2231
0.189	6.9	4800	0.8587	0.2159
0.1336	7.47	5200	0.9012	0.2101
0.0827	8.05	5600	0.9302	0.2100
0.0506	8.62	6000	0.9622	0.2063
0.0506	9.2	6400	0.9826	0.2062
0.0389	9.77	6800	0.9933	0.2067

Framework versions

Transformers 4.21.1
Pytorch 1.12.1
Datasets 2.4.0
Tokenizers 0.12.1

Bingsu/distilhubert-ko-zeroth

distilhubert-ko-zeroth

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Bingsu/
distilhubert-ko-zeroth