model_phoneme_onSet3

• eval_loss: 0.1119
• eval_0_precision: 1.0
• eval_0_recall: 0.9355
• eval_0_f1-score: 0.9667
• eval_0_support: 31
• eval_1_precision: 0.9231
• eval_1_recall: 1.0
• eval_1_f1-score: 0.9600
• eval_1_support: 24
• eval_2_precision: 1.0
• eval_2_recall: 0.9545
• eval_2_f1-score: 0.9767
• eval_2_support: 22
• eval_3_precision: 0.9524
• eval_3_recall: 1.0
• eval_3_f1-score: 0.9756
• eval_3_support: 20
• eval_accuracy: 0.9691
• eval_macro avg_precision: 0.9689
• eval_macro avg_recall: 0.9725
• eval_macro avg_f1-score: 0.9698
• eval_macro avg_support: 97
• eval_weighted avg_precision: 0.9711
• eval_weighted avg_recall: 0.9691
• eval_weighted avg_f1-score: 0.9691
• eval_weighted avg_support: 97
• eval_wer: 0.0986
• eval_mtrix: [[0, 1, 2, 3], [0, 29, 1, 0, 1], [1, 0, 24, 0, 0], [2, 0, 1, 21, 0], [3, 0, 0, 0, 20]]
• eval_runtime: 5.7078
• eval_samples_per_second: 16.994
• eval_steps_per_second: 2.278
• step: 0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 0.0003
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 200
• num_epochs: 70
• mixed_precision_training: Native AMP

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.0+cu116
• Datasets 2.8.0
• Tokenizers 0.13.2