he-cantillation
This model is a fine-tuned version of ivrit-ai/whisper-v2-pd1-e1 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1088
- Wer: 15.5669
- Avg Precision Exact: 0.8757
- Avg Recall Exact: 0.8740
- Avg F1 Exact: 0.8742
- Avg Precision Letter Shift: 0.8953
- Avg Recall Letter Shift: 0.8939
- Avg F1 Letter Shift: 0.8939
- Avg Precision Word Level: 0.8982
- Avg Recall Word Level: 0.8977
- Avg F1 Word Level: 0.8973
- Avg Precision Word Shift: 0.9445
- Avg Recall Word Shift: 0.9462
- Avg F1 Word Shift: 0.9446
- 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: 0.0909
- F1 Min Word Shift: 0.1111
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
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 3000
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer | Avg Precision Exact | Avg Recall Exact | Avg F1 Exact | Avg Precision Letter Shift | Avg Recall Letter Shift | Avg F1 Letter Shift | Avg Precision Word Level | Avg Recall Word Level | Avg F1 Word Level | Avg Precision Word Shift | Avg Recall Word Shift | Avg F1 Word Shift | Precision Median Exact | Recall Median Exact | F1 Median Exact | Precision Max Exact | Recall Max Exact | F1 Max Exact | Precision Min Exact | Recall Min Exact | F1 Min Exact | Precision Min Letter Shift | Recall Min Letter Shift | F1 Min Letter Shift | Precision Min Word Level | Recall Min Word Level | F1 Min Word Level | Precision Min Word Shift | Recall Min Word Shift | F1 Min Word Shift |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.1548 | 0.0517 | 1000 | 0.1831 | 26.0896 | 0.7735 | 0.7794 | 0.7755 | 0.8024 | 0.8089 | 0.8047 | 0.8073 | 0.8148 | 0.8101 | 0.8872 | 0.8993 | 0.8921 | 0.8462 | 0.8462 | 0.8462 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0769 | 0.0909 | 0.0833 |
0.0967 | 0.1033 | 2000 | 0.1297 | 18.6035 | 0.8415 | 0.8449 | 0.8425 | 0.8634 | 0.8672 | 0.8646 | 0.8676 | 0.8714 | 0.8688 | 0.9293 | 0.9355 | 0.9315 | 0.9091 | 0.9167 | 0.9091 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0769 | 0.0909 | 0.0833 |
0.0618 | 0.1550 | 3000 | 0.1088 | 15.5669 | 0.8757 | 0.8740 | 0.8742 | 0.8953 | 0.8939 | 0.8939 | 0.8982 | 0.8977 | 0.8973 | 0.9445 | 0.9462 | 0.9446 | 0.9231 | 0.9231 | 0.9286 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 | 0.0909 | 0.1111 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.2.1
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for cantillation/Teamim-large-v2_WeightDecay-0.001_Augmented_Combined-Data_date-09-07-2024_12-45
Base model
ivrit-ai/whisper-v2-pd1-e1