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hubert-classifier-aug-fold-4

Audio Classification
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hubert
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  1. README.md +109 -0
  2. model.safetensors +1 -1
  3. runs/Jul20_06-06-04_LAPTOP-1GID9RGH/events.out.tfevents.1721432063.LAPTOP-1GID9RGH.19084.9 +3 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: facebook/hubert-base-ls960
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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: hubert-classifier-aug-fold-4
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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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+ # hubert-classifier-aug-fold-4
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+
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+ This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5796
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+ - Accuracy: 0.8464
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+ - Precision: 0.8583
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+ - Recall: 0.8464
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+ - F1: 0.8439
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+ - Binary: 0.8937
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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: 0.0001
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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: 30
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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 | 0.13 | 50 | 3.8789 | 0.0553 | 0.0144 | 0.0553 | 0.0181 | 0.3219 |
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+ | No log | 0.27 | 100 | 3.4174 | 0.0904 | 0.0361 | 0.0904 | 0.0326 | 0.3583 |
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+ | No log | 0.4 | 150 | 3.1570 | 0.1350 | 0.0469 | 0.1350 | 0.0581 | 0.3893 |
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+ | No log | 0.54 | 200 | 2.9433 | 0.1930 | 0.0910 | 0.1930 | 0.1033 | 0.4305 |
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+ | No log | 0.67 | 250 | 2.6737 | 0.2132 | 0.1403 | 0.2132 | 0.1321 | 0.4498 |
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+ | No log | 0.81 | 300 | 2.4275 | 0.3266 | 0.2467 | 0.3266 | 0.2383 | 0.5259 |
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+ | No log | 0.94 | 350 | 2.1296 | 0.3941 | 0.3415 | 0.3941 | 0.3172 | 0.5744 |
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+ | 3.1825 | 1.08 | 400 | 2.0224 | 0.4642 | 0.4041 | 0.4642 | 0.3980 | 0.6205 |
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+ | 3.1825 | 1.21 | 450 | 1.7576 | 0.5304 | 0.4684 | 0.5304 | 0.4669 | 0.6719 |
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+ | 3.1825 | 1.35 | 500 | 1.5524 | 0.5830 | 0.5418 | 0.5830 | 0.5269 | 0.7086 |
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+ | 3.1825 | 1.48 | 550 | 1.4485 | 0.6248 | 0.6632 | 0.6248 | 0.5989 | 0.7383 |
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+ | 3.1825 | 1.62 | 600 | 1.3192 | 0.6383 | 0.6408 | 0.6383 | 0.5987 | 0.7476 |
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+ | 3.1825 | 1.75 | 650 | 1.1634 | 0.6964 | 0.7105 | 0.6964 | 0.6794 | 0.7877 |
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+ | 3.1825 | 1.89 | 700 | 1.1105 | 0.7301 | 0.7413 | 0.7301 | 0.7195 | 0.8093 |
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+ | 1.6698 | 2.02 | 750 | 1.0674 | 0.7314 | 0.7423 | 0.7314 | 0.7180 | 0.8115 |
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+ | 1.6698 | 2.16 | 800 | 1.0295 | 0.7314 | 0.7376 | 0.7314 | 0.7169 | 0.8128 |
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+ | 1.6698 | 2.29 | 850 | 0.9788 | 0.7355 | 0.7508 | 0.7355 | 0.7235 | 0.8148 |
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+ | 1.6698 | 2.43 | 900 | 0.9171 | 0.7503 | 0.7680 | 0.7503 | 0.7409 | 0.8250 |
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+ | 1.6698 | 2.56 | 950 | 0.7782 | 0.7908 | 0.7975 | 0.7908 | 0.7824 | 0.8533 |
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+ | 1.6698 | 2.7 | 1000 | 0.8368 | 0.7719 | 0.7872 | 0.7719 | 0.7624 | 0.8428 |
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+ | 1.6698 | 2.83 | 1050 | 0.8154 | 0.7692 | 0.7821 | 0.7692 | 0.7618 | 0.8393 |
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+ | 1.6698 | 2.96 | 1100 | 0.7900 | 0.7773 | 0.7894 | 0.7773 | 0.7686 | 0.8451 |
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+ | 1.0229 | 3.1 | 1150 | 0.7467 | 0.7949 | 0.8151 | 0.7949 | 0.7896 | 0.8582 |
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+ | 1.0229 | 3.23 | 1200 | 0.8610 | 0.7787 | 0.7916 | 0.7787 | 0.7693 | 0.8467 |
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+ | 1.0229 | 3.37 | 1250 | 0.7699 | 0.8057 | 0.8184 | 0.8057 | 0.8022 | 0.8655 |
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+ | 1.0229 | 3.5 | 1300 | 0.7546 | 0.8124 | 0.8338 | 0.8124 | 0.8108 | 0.8699 |
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+ | 1.0229 | 3.64 | 1350 | 0.7968 | 0.8003 | 0.8238 | 0.8003 | 0.7962 | 0.8626 |
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+ | 1.0229 | 3.77 | 1400 | 0.6990 | 0.8300 | 0.8471 | 0.8300 | 0.8271 | 0.8829 |
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+ | 1.0229 | 3.91 | 1450 | 0.7244 | 0.8219 | 0.8423 | 0.8219 | 0.8210 | 0.8750 |
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+ | 0.7454 | 4.04 | 1500 | 0.7213 | 0.8246 | 0.8426 | 0.8246 | 0.8219 | 0.8779 |
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+ | 0.7454 | 4.18 | 1550 | 0.8174 | 0.7922 | 0.8085 | 0.7922 | 0.7882 | 0.8549 |
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+ | 0.7454 | 4.31 | 1600 | 0.7212 | 0.8286 | 0.8475 | 0.8286 | 0.8243 | 0.8802 |
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+ | 0.7454 | 4.45 | 1650 | 0.6948 | 0.8327 | 0.8487 | 0.8327 | 0.8286 | 0.8830 |
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+ | 0.7454 | 4.58 | 1700 | 0.7873 | 0.8043 | 0.8237 | 0.8043 | 0.7998 | 0.8652 |
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+ | 0.7454 | 4.72 | 1750 | 0.7593 | 0.8124 | 0.8409 | 0.8124 | 0.8070 | 0.8709 |
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+ | 0.7454 | 4.85 | 1800 | 0.7766 | 0.8192 | 0.8362 | 0.8192 | 0.8154 | 0.8746 |
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+ | 0.7454 | 4.99 | 1850 | 0.7740 | 0.8205 | 0.8347 | 0.8205 | 0.8150 | 0.8767 |
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+ | 0.6044 | 5.12 | 1900 | 0.7932 | 0.8138 | 0.8279 | 0.8138 | 0.8071 | 0.8718 |
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+ | 0.6044 | 5.26 | 1950 | 0.8338 | 0.8205 | 0.8458 | 0.8205 | 0.8141 | 0.8756 |
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+ | 0.6044 | 5.39 | 2000 | 0.7471 | 0.8192 | 0.8341 | 0.8192 | 0.8123 | 0.8741 |
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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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