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cloudwalkerw
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wavlm-base_5

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_5
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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_5
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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: 0.4151
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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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+ | 0.3764 | 0.25 | 100 | 0.0277 | 0.9948 |
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+ | 0.1211 | 0.5 | 200 | 0.0297 | 0.9981 |
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+ | 0.2525 | 0.76 | 300 | 1.2840 | 0.9168 |
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+ | 0.784 | 1.01 | 400 | 0.3443 | 0.8974 |
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+ | 0.6053 | 1.26 | 500 | 0.3958 | 0.8974 |
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+ | 0.6038 | 1.51 | 600 | 0.4848 | 0.8974 |
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+ | 0.5996 | 1.76 | 700 | 0.3954 | 0.8974 |
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+ | 0.5914 | 2.02 | 800 | 0.3970 | 0.8974 |
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+ | 0.6077 | 2.27 | 900 | 0.4722 | 0.8974 |
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+ | 0.5991 | 2.52 | 1000 | 0.4362 | 0.8974 |
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+ | 0.5813 | 2.77 | 1100 | 0.3871 | 0.8974 |
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+ | 0.5953 | 3.02 | 1200 | 0.4013 | 0.8974 |
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+ | 0.5957 | 3.28 | 1300 | 0.4693 | 0.8974 |
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+ | 0.5852 | 3.53 | 1400 | 0.3879 | 0.8974 |
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+ | 0.6066 | 3.78 | 1500 | 0.4280 | 0.8974 |
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+ | 0.6085 | 4.03 | 1600 | 0.4359 | 0.8974 |
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+ | 0.5944 | 4.28 | 1700 | 0.4167 | 0.8974 |
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+ | 0.5994 | 4.54 | 1800 | 0.4139 | 0.8974 |
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+ | 0.5953 | 4.79 | 1900 | 0.4256 | 0.8974 |
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+ | 0.5929 | 5.04 | 2000 | 0.4371 | 0.8974 |
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+ | 0.6067 | 5.29 | 2100 | 0.4255 | 0.8974 |
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+ | 0.5944 | 5.55 | 2200 | 0.4121 | 0.8974 |
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+ | 0.5926 | 5.8 | 2300 | 0.4210 | 0.8974 |
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+ | 0.594 | 6.05 | 2400 | 0.4057 | 0.8974 |
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+ | 0.6042 | 6.3 | 2500 | 0.4252 | 0.8974 |
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+ | 0.5971 | 6.55 | 2600 | 0.3958 | 0.8974 |
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+ | 0.597 | 6.81 | 2700 | 0.4124 | 0.8974 |
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+ | 0.5816 | 7.06 | 2800 | 0.4101 | 0.8974 |
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+ | 0.5944 | 7.31 | 2900 | 0.4258 | 0.8974 |
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+ | 0.6053 | 7.56 | 3000 | 0.4415 | 0.8974 |
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+ | 0.5894 | 7.81 | 3100 | 0.4067 | 0.8974 |
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+ | 0.5987 | 8.07 | 3200 | 0.4109 | 0.8974 |
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+ | 0.5846 | 8.32 | 3300 | 0.4095 | 0.8974 |
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+ | 0.5982 | 8.57 | 3400 | 0.4187 | 0.8974 |
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+ | 0.5932 | 8.82 | 3500 | 0.4124 | 0.8974 |
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+ | 0.6007 | 9.07 | 3600 | 0.4212 | 0.8974 |
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+ | 0.6041 | 9.33 | 3700 | 0.4257 | 0.8974 |
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+ | 0.5859 | 9.58 | 3800 | 0.4176 | 0.8974 |
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+ | 0.5842 | 9.83 | 3900 | 0.4151 | 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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