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JBZhang2342
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speecht5_tts

Text-to-Audio
Transformers
TensorBoard
Safetensors
English
speecht5
en_accent,mozilla,t5,common_voice_1_0
Generated from Trainer
Inference Endpoints
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  1. README.md +67 -52
  2. model.safetensors +1 -1
  3. runs/Dec09_21-48-39_Threadripper/events.out.tfevents.1702176519.Threadripper +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -1,26 +1,21 @@
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  ---
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- language:
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- - en
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  license: mit
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  base_model: microsoft/speecht5_tts
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  tags:
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- - en_accent,mozilla,t5,common_voice_1_0
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  - generated_from_trainer
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- datasets:
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- - mozilla-foundation/common_voice_1_0
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  model-index:
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- - name: SpeechT5 TTS English Accented
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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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- # SpeechT5 TTS English Accented
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- This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the Common Voice dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3797
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  ## Model description
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@@ -46,53 +41,73 @@ The following hyperparameters were used during training:
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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: 500
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- - training_steps: 10000
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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 |
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- |:-------------:|:-----:|:-----:|:---------------:|
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- | No log | 2.34 | 250 | 0.5177 |
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- | 0.587 | 4.67 | 500 | 0.3719 |
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- | 0.587 | 7.01 | 750 | 0.3577 |
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- | 0.4026 | 9.35 | 1000 | 0.3580 |
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- | 0.4026 | 11.68 | 1250 | 0.3634 |
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- | 0.3733 | 14.02 | 1500 | 0.3597 |
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- | 0.3733 | 16.36 | 1750 | 0.3575 |
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- | 0.3643 | 18.69 | 2000 | 0.3580 |
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- | 0.3643 | 21.03 | 2250 | 0.3562 |
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- | 0.35 | 23.36 | 2500 | 0.3566 |
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- | 0.35 | 25.7 | 2750 | 0.3630 |
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- | 0.3433 | 28.04 | 3000 | 0.3551 |
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- | 0.3433 | 30.37 | 3250 | 0.3601 |
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- | 0.3412 | 32.71 | 3500 | 0.3648 |
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- | 0.3412 | 35.05 | 3750 | 0.3667 |
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- | 0.3377 | 37.38 | 4000 | 0.3716 |
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- | 0.3377 | 39.72 | 4250 | 0.3759 |
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- | 0.333 | 42.06 | 4500 | 0.3709 |
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- | 0.333 | 44.39 | 4750 | 0.3707 |
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- | 0.3319 | 46.73 | 5000 | 0.3722 |
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- | 0.3319 | 49.07 | 5250 | 0.3678 |
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- | 0.328 | 51.4 | 5500 | 0.3653 |
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- | 0.328 | 53.74 | 5750 | 0.3831 |
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- | 0.3235 | 56.07 | 6000 | 0.3731 |
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- | 0.3235 | 58.41 | 6250 | 0.3809 |
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- | 0.3241 | 60.75 | 6500 | 0.3791 |
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- | 0.3241 | 63.08 | 6750 | 0.3799 |
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- | 0.3197 | 65.42 | 7000 | 0.3759 |
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- | 0.3197 | 67.76 | 7250 | 0.3734 |
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- | 0.3202 | 70.09 | 7500 | 0.3717 |
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- | 0.3202 | 72.43 | 7750 | 0.3811 |
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- | 0.3189 | 74.77 | 8000 | 0.3821 |
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- | 0.3189 | 77.1 | 8250 | 0.3884 |
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- | 0.3177 | 79.44 | 8500 | 0.3811 |
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- | 0.3177 | 81.78 | 8750 | 0.3729 |
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- | 0.3162 | 84.11 | 9000 | 0.3803 |
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- | 0.3162 | 86.45 | 9250 | 0.3835 |
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- | 0.3154 | 88.79 | 9500 | 0.3845 |
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- | 0.3154 | 91.12 | 9750 | 0.3815 |
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- | 0.3143 | 93.46 | 10000 | 0.3797 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
1
  ---
 
 
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  license: mit
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  base_model: microsoft/speecht5_tts
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  tags:
 
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  - generated_from_trainer
 
 
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  model-index:
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+ - name: speecht5_tts
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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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+ # speecht5_tts
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+ This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3812
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  ## Model description
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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: 500
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+ - training_steps: 15000
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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 |
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+ |:-------------:|:------:|:-----:|:---------------:|
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+ | No log | 2.34 | 250 | 0.5148 |
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+ | 0.5893 | 4.67 | 500 | 0.3786 |
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+ | 0.5893 | 7.01 | 750 | 0.3621 |
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+ | 0.4015 | 9.35 | 1000 | 0.3600 |
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+ | 0.4015 | 11.68 | 1250 | 0.3701 |
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+ | 0.3739 | 14.02 | 1500 | 0.3612 |
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+ | 0.3739 | 16.36 | 1750 | 0.3626 |
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+ | 0.3634 | 18.69 | 2000 | 0.3499 |
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+ | 0.3634 | 21.03 | 2250 | 0.3549 |
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+ | 0.3499 | 23.36 | 2500 | 0.3600 |
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+ | 0.3499 | 25.7 | 2750 | 0.3533 |
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+ | 0.3428 | 28.04 | 3000 | 0.3652 |
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+ | 0.3428 | 30.37 | 3250 | 0.3541 |
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+ | 0.3407 | 32.71 | 3500 | 0.3579 |
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+ | 0.3407 | 35.05 | 3750 | 0.3550 |
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+ | 0.3368 | 37.38 | 4000 | 0.3624 |
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+ | 0.3368 | 39.72 | 4250 | 0.3621 |
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+ | 0.3315 | 42.06 | 4500 | 0.3577 |
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+ | 0.3315 | 44.39 | 4750 | 0.3620 |
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+ | 0.3305 | 46.73 | 5000 | 0.3665 |
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+ | 0.3305 | 49.07 | 5250 | 0.3641 |
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+ | 0.3273 | 51.4 | 5500 | 0.3563 |
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+ | 0.3273 | 53.74 | 5750 | 0.3579 |
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+ | 0.3228 | 56.07 | 6000 | 0.3615 |
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+ | 0.3228 | 58.41 | 6250 | 0.3606 |
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+ | 0.3227 | 60.75 | 6500 | 0.3647 |
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+ | 0.3227 | 63.08 | 6750 | 0.3647 |
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+ | 0.3183 | 65.42 | 7000 | 0.3619 |
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+ | 0.3183 | 67.76 | 7250 | 0.3786 |
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+ | 0.3184 | 70.09 | 7500 | 0.3731 |
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+ | 0.3184 | 72.43 | 7750 | 0.3630 |
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+ | 0.3177 | 74.77 | 8000 | 0.3647 |
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+ | 0.3177 | 77.1 | 8250 | 0.3668 |
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+ | 0.3159 | 79.44 | 8500 | 0.3624 |
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+ | 0.3159 | 81.78 | 8750 | 0.3742 |
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+ | 0.3129 | 84.11 | 9000 | 0.3722 |
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+ | 0.3129 | 86.45 | 9250 | 0.3755 |
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+ | 0.3124 | 88.79 | 9500 | 0.3693 |
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+ | 0.3124 | 91.12 | 9750 | 0.3707 |
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+ | 0.3094 | 93.46 | 10000 | 0.3808 |
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+ | 0.3094 | 95.79 | 10250 | 0.3696 |
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+ | 0.3116 | 98.13 | 10500 | 0.3773 |
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+ | 0.3116 | 100.47 | 10750 | 0.3796 |
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+ | 0.3076 | 102.8 | 11000 | 0.3705 |
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+ | 0.3076 | 105.14 | 11250 | 0.3718 |
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+ | 0.3104 | 107.48 | 11500 | 0.3792 |
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+ | 0.3104 | 109.81 | 11750 | 0.3714 |
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+ | 0.3078 | 112.15 | 12000 | 0.3765 |
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+ | 0.3078 | 114.49 | 12250 | 0.3803 |
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+ | 0.3064 | 116.82 | 12500 | 0.3792 |
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+ | 0.3064 | 119.16 | 12750 | 0.3803 |
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+ | 0.3087 | 121.5 | 13000 | 0.3806 |
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+ | 0.3087 | 123.83 | 13250 | 0.3821 |
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+ | 0.3064 | 126.17 | 13500 | 0.3795 |
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+ | 0.3064 | 128.5 | 13750 | 0.3766 |
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+ | 0.3066 | 130.84 | 14000 | 0.3780 |
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+ | 0.3066 | 133.18 | 14250 | 0.3858 |
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+ | 0.3081 | 135.51 | 14500 | 0.3812 |
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+ | 0.3081 | 137.85 | 14750 | 0.3829 |
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+ | 0.3064 | 140.19 | 15000 | 0.3812 |
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  ### Framework versions
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