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cloudwalkerw
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wavlm-base_3

Audio Classification
Transformers
PyTorch
wavlm
Generated from Trainer
Inference Endpoints
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README.md ADDED
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+ ---
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+ base_model: microsoft/wavlm-base
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: wavlm-base_3
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+ results: []
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+ ---
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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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+
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+ # wavlm-base_3
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+
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+ This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3325
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+ - Accuracy: 0.8974
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0003
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+ - train_batch_size: 16
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+ - eval_batch_size: 2
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+ - seed: 0
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 64
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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_ratio: 0.1
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+ - num_epochs: 10.0
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.4161 | 0.25 | 100 | 0.3295 | 0.8974 |
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+ | 0.3196 | 0.5 | 200 | 0.3312 | 0.8974 |
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+ | 0.3391 | 0.76 | 300 | 0.3353 | 0.8974 |
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+ | 0.3285 | 1.01 | 400 | 0.3322 | 0.8974 |
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+ | 0.3354 | 1.26 | 500 | 0.3366 | 0.8974 |
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+ | 0.3344 | 1.51 | 600 | 0.3315 | 0.8974 |
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+ | 0.3343 | 1.76 | 700 | 0.3308 | 0.8974 |
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+ | 0.325 | 2.02 | 800 | 0.3382 | 0.8974 |
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+ | 0.34 | 2.27 | 900 | 0.3314 | 0.8974 |
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+ | 0.3333 | 2.52 | 1000 | 0.3388 | 0.8974 |
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+ | 0.318 | 2.77 | 1100 | 0.3371 | 0.8974 |
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+ | 0.3281 | 3.02 | 1200 | 0.3362 | 0.8974 |
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+ | 0.3293 | 3.28 | 1300 | 0.3307 | 0.8974 |
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+ | 0.3175 | 3.53 | 1400 | 0.3357 | 0.8974 |
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+ | 0.3415 | 3.78 | 1500 | 0.3321 | 0.8974 |
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+ | 0.341 | 4.03 | 1600 | 0.3307 | 0.8974 |
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+ | 0.3285 | 4.28 | 1700 | 0.3308 | 0.8974 |
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+ | 0.3337 | 4.54 | 1800 | 0.3308 | 0.8974 |
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+ | 0.3276 | 4.79 | 1900 | 0.3307 | 0.8974 |
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+ | 0.3248 | 5.04 | 2000 | 0.3311 | 0.8974 |
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+ | 0.3371 | 5.29 | 2100 | 0.3317 | 0.8974 |
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+ | 0.3261 | 5.55 | 2200 | 0.3315 | 0.8974 |
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+ | 0.3277 | 5.8 | 2300 | 0.3323 | 0.8974 |
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+ | 0.3297 | 6.05 | 2400 | 0.3321 | 0.8974 |
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+ | 0.3397 | 6.3 | 2500 | 0.3316 | 0.8974 |
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+ | 0.3313 | 6.55 | 2600 | 0.3376 | 0.8974 |
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+ | 0.3297 | 6.81 | 2700 | 0.3326 | 0.8974 |
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+ | 0.3148 | 7.06 | 2800 | 0.3326 | 0.8974 |
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+ | 0.33 | 7.31 | 2900 | 0.3307 | 0.8974 |
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+ | 0.3373 | 7.56 | 3000 | 0.3358 | 0.8974 |
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+ | 0.3229 | 7.81 | 3100 | 0.3315 | 0.8974 |
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+ | 0.3311 | 8.07 | 3200 | 0.3330 | 0.8974 |
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+ | 0.32 | 8.32 | 3300 | 0.3329 | 0.8974 |
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+ | 0.3303 | 8.57 | 3400 | 0.3333 | 0.8974 |
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+ | 0.3268 | 8.82 | 3500 | 0.3325 | 0.8974 |
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+ | 0.3362 | 9.07 | 3600 | 0.3314 | 0.8974 |
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+ | 0.3391 | 9.33 | 3700 | 0.3309 | 0.8974 |
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+ | 0.3233 | 9.58 | 3800 | 0.3319 | 0.8974 |
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+ | 0.3196 | 9.83 | 3900 | 0.3325 | 0.8974 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.34.0.dev0
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+ - Pytorch 2.0.0.post302
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+ - Datasets 2.14.5
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+ - Tokenizers 0.13.3
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