cantillation/Teamim-tiny_WeightDecay-0.05_Combined-Data_date-17-07-2024_10-10

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he-cantillation

This model is a fine-tuned version of openai/whisper-tiny on an unknown dataset. It achieves the following results on the evaluation set:

• —Loss: 0.2642
• —Wer: 16.6210
• —Avg Precision Exact: 0.8369
• —Avg Recall Exact: 0.8382
• —Avg F1 Exact: 0.8371
• —Avg Precision Letter Shift: 0.8586
• —Avg Recall Letter Shift: 0.8599
• —Avg F1 Letter Shift: 0.8588
• —Avg Precision Word Level: 0.8633
• —Avg Recall Word Level: 0.8652
• —Avg F1 Word Level: 0.8637
• —Avg Precision Word Shift: 0.9480
• —Avg Recall Word Shift: 0.9510
• —Avg F1 Word Shift: 0.9488
• —Precision Median Exact: 0.9231
• —Recall Median Exact: 0.9231
• —F1 Median Exact: 0.9286
• —Precision Max Exact: 1.0
• —Recall Max Exact: 1.0
• —F1 Max Exact: 1.0
• —Precision Min Exact: 0.0
• —Recall Min Exact: 0.0
• —F1 Min Exact: 0.0
• —Precision Min Letter Shift: 0.0
• —Recall Min Letter Shift: 0.0
• —F1 Min Letter Shift: 0.0
• —Precision Min Word Level: 0.0
• —Recall Min Word Level: 0.0
• —F1 Min Word Level: 0.0
• —Precision Min Word Shift: 0.1429
• —Recall Min Word Shift: 0.125
• —F1 Min Word Shift: 0.1333

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: 1e-05
• —trainbatchsize: 8
• —evalbatchsize: 32
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —lrschedulerwarmup_steps: 500
• —training_steps: 500000
• —mixedprecisiontraining: Native AMP

Training results

Framework versions

• —Transformers 4.41.2
• —Pytorch 2.2.1
• —Datasets 2.20.0
• —Tokenizers 0.19.1