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--- |
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language: |
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- he |
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base_model: cantillation/Teamim-large-v2_WeightDecay-0.05_Augmented_Combined-Data_date-09-07-2024_16-25 |
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tags: |
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- hf-asr-leaderboard |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: he-cantillation |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# he-cantillation |
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This model is a fine-tuned version of [cantillation/Teamim-large-v2_WeightDecay-0.05_Augmented_Combined-Data_date-09-07-2024_16-25](https://huggingface.co/cantillation/Teamim-large-v2_WeightDecay-0.05_Augmented_Combined-Data_date-09-07-2024_16-25) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6664 |
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- Wer: 34.2105 |
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- Avg Precision Exact: 0.3970 |
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- Avg Recall Exact: 0.4021 |
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- Avg F1 Exact: 0.3994 |
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- Avg Precision Letter Shift: 0.3970 |
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- Avg Recall Letter Shift: 0.4021 |
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- Avg F1 Letter Shift: 0.3994 |
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- Avg Precision Word Level: 0.3905 |
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- Avg Recall Word Level: 0.3956 |
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- Avg F1 Word Level: 0.3929 |
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- Avg Precision Word Shift: 0.5197 |
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- Avg Recall Word Shift: 0.5402 |
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- Avg F1 Word Shift: 0.5292 |
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- Precision Median Exact: 0.3970 |
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- Recall Median Exact: 0.4021 |
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- F1 Median Exact: 0.3994 |
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- Precision Max Exact: 0.7273 |
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- Recall Max Exact: 0.7273 |
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- F1 Max Exact: 0.7273 |
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- Precision Min Exact: 0.0667 |
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- Recall Min Exact: 0.0769 |
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- F1 Min Exact: 0.0714 |
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- Precision Min Letter Shift: 0.0667 |
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- Recall Min Letter Shift: 0.0769 |
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- F1 Min Letter Shift: 0.0714 |
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- Precision Min Word Level: 0.0667 |
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- Recall Min Word Level: 0.0769 |
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- F1 Min Word Level: 0.0714 |
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- Precision Min Word Shift: 0.2667 |
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- Recall Min Word Shift: 0.3077 |
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- F1 Min Word Shift: 0.2857 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 6 |
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- training_steps: 40 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| 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 | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|:------------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:| |
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| No log | 1.0 | 2 | 1.0938 | 76.3158 | 0.25 | 0.2619 | 0.2558 | 0.25 | 0.2619 | 0.2558 | 0.2619 | 0.2619 | 0.2619 | 0.2727 | 0.2857 | 0.2791 | 0.25 | 0.2619 | 0.2558 | 0.5 | 0.5238 | 0.5116 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
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| No log | 2.0 | 4 | 0.7695 | 52.6316 | 0.25 | 0.2391 | 0.2444 | 0.25 | 0.2391 | 0.2444 | 0.2619 | 0.25 | 0.2558 | 0.3409 | 0.3261 | 0.3333 | 0.25 | 0.2391 | 0.2444 | 0.5 | 0.4783 | 0.4889 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
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| No log | 3.0 | 6 | 0.6176 | 52.6316 | 0.2955 | 0.2826 | 0.2889 | 0.2955 | 0.2826 | 0.2889 | 0.2857 | 0.2727 | 0.2791 | 0.3515 | 0.3401 | 0.3456 | 0.2955 | 0.2826 | 0.2889 | 0.5909 | 0.5652 | 0.5778 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0667 | 0.0714 | 0.0690 | |
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| No log | 4.0 | 8 | 0.6977 | 50.0 | 0.25 | 0.25 | 0.25 | 0.25 | 0.25 | 0.25 | 0.2381 | 0.2381 | 0.2381 | 0.3352 | 0.3497 | 0.3417 | 0.25 | 0.25 | 0.25 | 0.5 | 0.5 | 0.5 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.125 | 0.1538 | 0.1379 | |
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| No log | 5.0 | 10 | 0.6345 | 50.0 | 0.25 | 0.25 | 0.25 | 0.25 | 0.25 | 0.25 | 0.2381 | 0.2381 | 0.2381 | 0.3352 | 0.3497 | 0.3417 | 0.25 | 0.25 | 0.25 | 0.5 | 0.5 | 0.5 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.125 | 0.1538 | 0.1379 | |
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| No log | 6.0 | 12 | 0.6303 | 47.3684 | 0.2727 | 0.2857 | 0.2791 | 0.2727 | 0.2857 | 0.2791 | 0.2619 | 0.275 | 0.2683 | 0.3580 | 0.3864 | 0.3713 | 0.2727 | 0.2857 | 0.2791 | 0.5455 | 0.5714 | 0.5581 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.125 | 0.1538 | 0.1379 | |
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| No log | 7.0 | 14 | 0.6193 | 44.7368 | 0.2955 | 0.3095 | 0.3023 | 0.2955 | 0.3095 | 0.3023 | 0.2857 | 0.3 | 0.2927 | 0.4119 | 0.4487 | 0.4290 | 0.2955 | 0.3095 | 0.3023 | 0.5909 | 0.6190 | 0.6047 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1875 | 0.2308 | 0.2069 | |
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| No log | 8.0 | 16 | 0.6194 | 42.1053 | 0.3182 | 0.3182 | 0.3182 | 0.3182 | 0.3182 | 0.3182 | 0.3095 | 0.3095 | 0.3095 | 0.4347 | 0.4563 | 0.4444 | 0.3182 | 0.3182 | 0.3182 | 0.6364 | 0.6364 | 0.6364 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1875 | 0.2308 | 0.2069 | |
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| No log | 9.0 | 18 | 0.6302 | 39.4737 | 0.3409 | 0.3409 | 0.3409 | 0.3409 | 0.3409 | 0.3409 | 0.3333 | 0.3333 | 0.3333 | 0.4574 | 0.4790 | 0.4671 | 0.3409 | 0.3409 | 0.3409 | 0.6818 | 0.6818 | 0.6818 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1875 | 0.2308 | 0.2069 | |
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| No log | 10.0 | 20 | 0.6424 | 39.4737 | 0.3409 | 0.3409 | 0.3409 | 0.3409 | 0.3409 | 0.3409 | 0.3333 | 0.3333 | 0.3333 | 0.4574 | 0.4790 | 0.4671 | 0.3409 | 0.3409 | 0.3409 | 0.6818 | 0.6818 | 0.6818 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1875 | 0.2308 | 0.2069 | |
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| No log | 11.0 | 22 | 0.6514 | 36.8421 | 0.3742 | 0.3794 | 0.3766 | 0.3742 | 0.3794 | 0.3766 | 0.3667 | 0.3718 | 0.3690 | 0.4970 | 0.5175 | 0.5065 | 0.3742 | 0.3794 | 0.3766 | 0.6818 | 0.6818 | 0.6818 | 0.0667 | 0.0769 | 0.0714 | 0.0667 | 0.0769 | 0.0714 | 0.0667 | 0.0769 | 0.0714 | 0.2667 | 0.3077 | 0.2857 | |
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| No log | 12.0 | 24 | 0.6571 | 36.8421 | 0.3742 | 0.3794 | 0.3766 | 0.3742 | 0.3794 | 0.3766 | 0.3667 | 0.3718 | 0.3690 | 0.4970 | 0.5175 | 0.5065 | 0.3742 | 0.3794 | 0.3766 | 0.6818 | 0.6818 | 0.6818 | 0.0667 | 0.0769 | 0.0714 | 0.0667 | 0.0769 | 0.0714 | 0.0667 | 0.0769 | 0.0714 | 0.2667 | 0.3077 | 0.2857 | |
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| 0.1235 | 13.0 | 26 | 0.6607 | 36.8421 | 0.3742 | 0.3794 | 0.3766 | 0.3742 | 0.3794 | 0.3766 | 0.3667 | 0.3718 | 0.3690 | 0.4970 | 0.5175 | 0.5065 | 0.3742 | 0.3794 | 0.3766 | 0.6818 | 0.6818 | 0.6818 | 0.0667 | 0.0769 | 0.0714 | 0.0667 | 0.0769 | 0.0714 | 0.0667 | 0.0769 | 0.0714 | 0.2667 | 0.3077 | 0.2857 | |
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| 0.1235 | 14.0 | 28 | 0.6629 | 36.8421 | 0.3742 | 0.3794 | 0.3766 | 0.3742 | 0.3794 | 0.3766 | 0.3667 | 0.3718 | 0.3690 | 0.4970 | 0.5175 | 0.5065 | 0.3742 | 0.3794 | 0.3766 | 0.6818 | 0.6818 | 0.6818 | 0.0667 | 0.0769 | 0.0714 | 0.0667 | 0.0769 | 0.0714 | 0.0667 | 0.0769 | 0.0714 | 0.2667 | 0.3077 | 0.2857 | |
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| 0.1235 | 15.0 | 30 | 0.6644 | 36.8421 | 0.3742 | 0.3794 | 0.3766 | 0.3742 | 0.3794 | 0.3766 | 0.3667 | 0.3718 | 0.3690 | 0.4970 | 0.5175 | 0.5065 | 0.3742 | 0.3794 | 0.3766 | 0.6818 | 0.6818 | 0.6818 | 0.0667 | 0.0769 | 0.0714 | 0.0667 | 0.0769 | 0.0714 | 0.0667 | 0.0769 | 0.0714 | 0.2667 | 0.3077 | 0.2857 | |
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| 0.1235 | 16.0 | 32 | 0.6652 | 36.8421 | 0.3742 | 0.3794 | 0.3766 | 0.3742 | 0.3794 | 0.3766 | 0.3667 | 0.3718 | 0.3690 | 0.4970 | 0.5175 | 0.5065 | 0.3742 | 0.3794 | 0.3766 | 0.6818 | 0.6818 | 0.6818 | 0.0667 | 0.0769 | 0.0714 | 0.0667 | 0.0769 | 0.0714 | 0.0667 | 0.0769 | 0.0714 | 0.2667 | 0.3077 | 0.2857 | |
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| 0.1235 | 17.0 | 34 | 0.6662 | 36.8421 | 0.3742 | 0.3794 | 0.3766 | 0.3742 | 0.3794 | 0.3766 | 0.3667 | 0.3718 | 0.3690 | 0.4970 | 0.5175 | 0.5065 | 0.3742 | 0.3794 | 0.3766 | 0.6818 | 0.6818 | 0.6818 | 0.0667 | 0.0769 | 0.0714 | 0.0667 | 0.0769 | 0.0714 | 0.0667 | 0.0769 | 0.0714 | 0.2667 | 0.3077 | 0.2857 | |
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| 0.1235 | 18.0 | 36 | 0.6663 | 34.2105 | 0.3970 | 0.4021 | 0.3994 | 0.3970 | 0.4021 | 0.3994 | 0.3905 | 0.3956 | 0.3929 | 0.5197 | 0.5402 | 0.5292 | 0.3970 | 0.4021 | 0.3994 | 0.7273 | 0.7273 | 0.7273 | 0.0667 | 0.0769 | 0.0714 | 0.0667 | 0.0769 | 0.0714 | 0.0667 | 0.0769 | 0.0714 | 0.2667 | 0.3077 | 0.2857 | |
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| 0.1235 | 19.0 | 38 | 0.6665 | 34.2105 | 0.3970 | 0.4021 | 0.3994 | 0.3970 | 0.4021 | 0.3994 | 0.3905 | 0.3956 | 0.3929 | 0.5197 | 0.5402 | 0.5292 | 0.3970 | 0.4021 | 0.3994 | 0.7273 | 0.7273 | 0.7273 | 0.0667 | 0.0769 | 0.0714 | 0.0667 | 0.0769 | 0.0714 | 0.0667 | 0.0769 | 0.0714 | 0.2667 | 0.3077 | 0.2857 | |
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| 0.1235 | 20.0 | 40 | 0.6664 | 34.2105 | 0.3970 | 0.4021 | 0.3994 | 0.3970 | 0.4021 | 0.3994 | 0.3905 | 0.3956 | 0.3929 | 0.5197 | 0.5402 | 0.5292 | 0.3970 | 0.4021 | 0.3994 | 0.7273 | 0.7273 | 0.7273 | 0.0667 | 0.0769 | 0.0714 | 0.0667 | 0.0769 | 0.0714 | 0.0667 | 0.0769 | 0.0714 | 0.2667 | 0.3077 | 0.2857 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.2.1 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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