Teamim-large-v2_WeightDecay-0.001_Augmented_Combined-Data_date-09-07-2024_12-45 – AI Model by cantillation | AlphaNeural AI

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Teamim-large-v2_WeightDecay-0.001_Augmented_Combined-Data_date-09-07-2024_12-45

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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