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Teamim-large-v2_Random-True_date-08-06-2024_20-46-59 – AI Model by cantillation | AlphaNeural AI
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cantillation
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Teamim-large-v2_Random-True_date-08-06-2024_20-46-59
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transformers
tensorboard
safetensors
whisper
automatic-speech-recognition
hf-asr-leaderboard
generated_from_trainer
he
cantillation/Teamim-large-v2_Random-True_OldData_date-07-06-2024_16-07-57
finetune
endpoints_compatible
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he-cantillation
This model is a fine-tuned version of
cantillation/Teamim-large-v2_Random-True_OldData_date-07-06-2024_16-07-57
on an unknown dataset. It achieves the following results on the evaluation set:
eval_loss: 0.0899
eval_wer: 9.8928
eval_avg_precision_Exact: 0.9232
eval_avg_recall_Exact: 0.9228
eval_avg_f1_Exact: 0.9227
eval_avg_precision_Letter_Shift: 0.9383
eval_avg_recall_Letter_Shift: 0.9381
eval_avg_f1_Letter_Shift: 0.9379
eval_avg_precision_Word_Level: 0.9405
eval_avg_recall_Word_Level: 0.9404
eval_avg_f1_Word_Level: 0.9401
eval_avg_precision_Word_Shift: 0.9772
eval_avg_recall_Word_Shift: 0.9777
eval_avg_f1_Word_Shift: 0.9771
eval_precision_median_exact: 1.0
eval_recall_median_exact: 1.0
eval_f1_median_exact: 1.0
eval_precision_max_exact: 1.0
eval_recall_max_exact: 1.0
eval_f1_max_exact: 1.0
eval_precision_min_Exact: 0.0
eval_recall_min_Exact: 0.0
eval_f1_min_Exact: 0.0
eval_precision_min_Letter_Shift: 0.0
eval_recall_min_Letter_Shift: 0.0
eval_f1_min_Letter_Shift: 0.0
eval_precision_min_Word_Level: 0.0
eval_recall_min_Word_Level: 0.0
eval_f1_min_Word_Level: 0.0
eval_precision_min_Word_Shift: 0.1429
eval_recall_min_Word_Shift: 0.1
eval_f1_min_Word_Shift: 0.1176
eval_runtime: 2336.8917
eval_samples_per_second: 1.152
eval_steps_per_second: 0.036
epoch: 0.56
step: 7000
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: 50
training_steps: 8000
mixed_precision_training: Native AMP
Framework versions
Transformers 4.42.0.dev0
Pytorch 1.13.1+cu117
Datasets 2.16.1
Tokenizers 0.19.1
