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sewd-classifier-aug-large

Audio Classification
Transformers
TensorBoard
Safetensors
sew-d
Generated from Trainer
Inference Endpoints
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: asapp/sew-d-tiny-100k-ft-ls100h
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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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+ - precision
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+ - recall
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+ - f1
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+ model-index:
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+ - name: sewd-classifier-aug-large
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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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+ # sewd-classifier-aug-large
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+
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+ This model is a fine-tuned version of [asapp/sew-d-tiny-100k-ft-ls100h](https://huggingface.co/asapp/sew-d-tiny-100k-ft-ls100h) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 3.2094
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+ - Accuracy: 0.1712
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+ - Precision: 0.0867
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+ - Recall: 0.1712
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+ - F1: 0.0887
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+ - Binary: 0.4166
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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: 1e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 128
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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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+ - num_epochs: 10
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+ - mixed_precision_training: Native AMP
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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 | Precision | Recall | F1 | Binary |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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+ | No log | 0.1 | 50 | 4.4166 | 0.0108 | 0.0010 | 0.0108 | 0.0013 | 0.1291 |
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+ | No log | 0.2 | 100 | 4.3510 | 0.0135 | 0.0069 | 0.0135 | 0.0026 | 0.1367 |
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+ | No log | 0.29 | 150 | 4.2531 | 0.0323 | 0.0016 | 0.0323 | 0.0030 | 0.2372 |
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+ | No log | 0.39 | 200 | 4.1748 | 0.0350 | 0.0022 | 0.0350 | 0.0039 | 0.2779 |
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+ | No log | 0.49 | 250 | 4.0970 | 0.0431 | 0.0076 | 0.0431 | 0.0094 | 0.2969 |
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+ | No log | 0.59 | 300 | 4.0268 | 0.0499 | 0.0179 | 0.0499 | 0.0116 | 0.3198 |
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+ | No log | 0.69 | 350 | 3.9648 | 0.0472 | 0.0029 | 0.0472 | 0.0054 | 0.3175 |
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+ | No log | 0.78 | 400 | 3.8977 | 0.0553 | 0.0279 | 0.0553 | 0.0164 | 0.3283 |
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+ | No log | 0.88 | 450 | 3.8290 | 0.0768 | 0.0239 | 0.0768 | 0.0230 | 0.3437 |
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+ | No log | 0.98 | 500 | 3.7650 | 0.0701 | 0.0313 | 0.0701 | 0.0252 | 0.3418 |
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+ | 4.166 | 1.08 | 550 | 3.7060 | 0.0997 | 0.0444 | 0.0997 | 0.0418 | 0.3625 |
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+ | 4.166 | 1.18 | 600 | 3.6526 | 0.1132 | 0.0497 | 0.1132 | 0.0501 | 0.3733 |
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+ | 4.166 | 1.27 | 650 | 3.5931 | 0.1132 | 0.0433 | 0.1132 | 0.0509 | 0.3732 |
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+ | 4.166 | 1.37 | 700 | 3.5467 | 0.1240 | 0.0664 | 0.1240 | 0.0558 | 0.3805 |
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+ | 4.166 | 1.47 | 750 | 3.4981 | 0.1213 | 0.0589 | 0.1213 | 0.0553 | 0.3792 |
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+ | 4.166 | 1.57 | 800 | 3.4544 | 0.1361 | 0.0606 | 0.1361 | 0.0641 | 0.3904 |
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+ | 4.166 | 1.67 | 850 | 3.4081 | 0.1456 | 0.0557 | 0.1456 | 0.0655 | 0.3980 |
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+ | 4.166 | 1.76 | 900 | 3.3696 | 0.1536 | 0.0812 | 0.1536 | 0.0786 | 0.4040 |
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+ | 4.166 | 1.86 | 950 | 3.3213 | 0.1523 | 0.0609 | 0.1523 | 0.0745 | 0.4038 |
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+ | 4.166 | 1.96 | 1000 | 3.2814 | 0.1779 | 0.0991 | 0.1779 | 0.0922 | 0.4217 |
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+ | 3.6654 | 2.06 | 1050 | 3.2435 | 0.1550 | 0.0883 | 0.1550 | 0.0737 | 0.4061 |
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+ | 3.6654 | 2.16 | 1100 | 3.2094 | 0.1712 | 0.0867 | 0.1712 | 0.0887 | 0.4166 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.38.2
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+ - Pytorch 2.3.0
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+ - Datasets 2.19.1
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+ - Tokenizers 0.15.1
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