model_broadclass_onSet3

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

• eval_loss: 0.1389
• eval_0_precision: 1.0
• eval_0_recall: 1.0
• eval_0_f1-score: 1.0
• eval_0_support: 23
• eval_1_precision: 0.9697
• eval_1_recall: 0.9697
• eval_1_f1-score: 0.9697
• eval_1_support: 33
• eval_2_precision: 1.0
• eval_2_recall: 1.0
• eval_2_f1-score: 1.0
• eval_2_support: 26
• eval_3_precision: 0.9333
• eval_3_recall: 0.9333
• eval_3_f1-score: 0.9333
• eval_3_support: 15
• eval_accuracy: 0.9794
• eval_macro avg_precision: 0.9758
• eval_macro avg_recall: 0.9758
• eval_macro avg_f1-score: 0.9758
• eval_macro avg_support: 97
• eval_weighted avg_precision: 0.9794
• eval_weighted avg_recall: 0.9794
• eval_weighted avg_f1-score: 0.9794
• eval_weighted avg_support: 97
• eval_wer: 0.1037
• eval_mtrix: [[0, 1, 2, 3], [0, 23, 0, 0, 0], [1, 0, 32, 0, 1], [2, 0, 0, 26, 0], [3, 0, 1, 0, 14]]
• eval_runtime: 5.6481
• eval_samples_per_second: 17.174
• eval_steps_per_second: 2.302
• step: 0

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: 0.0003
• 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: 80
• mixed_precision_training: Native AMP

Framework versions

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