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callmesan
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vakyansh-wav2vec2-hindi-him-4200-audio-abuse-feature

Audio Classification
Transformers
PyTorch
wav2vec2
Generated from Trainer
Inference Endpoints
Model card Files Community
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+ ---
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+ license: mit
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+ base_model: Harveenchadha/vakyansh-wav2vec2-hindi-him-4200
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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: vakyansh-wav2vec2-hindi-him-4200-audio-abuse-feature
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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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+ # vakyansh-wav2vec2-hindi-him-4200-audio-abuse-feature
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+
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+ This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-hindi-him-4200](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-hindi-him-4200) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6541
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+ - Accuracy: 0.6938
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+ - Macro F1-score: 0.6938
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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: 2e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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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: 50
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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 | Macro F1-score |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------------:|
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+ | 6.7199 | 0.77 | 10 | 6.7179 | 0.0 | 0.0 |
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+ | 6.699 | 1.54 | 20 | 6.6606 | 0.1192 | 0.0134 |
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+ | 6.6237 | 2.31 | 30 | 6.5410 | 0.4499 | 0.0852 |
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+ | 6.4723 | 3.08 | 40 | 6.3277 | 0.5014 | 0.2226 |
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+ | 6.2904 | 3.85 | 50 | 6.0526 | 0.5041 | 0.3351 |
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+ | 6.0074 | 4.62 | 60 | 5.7219 | 0.5041 | 0.3351 |
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+ | 5.7233 | 5.38 | 70 | 5.3914 | 0.5041 | 0.3351 |
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+ | 5.4426 | 6.15 | 80 | 5.0902 | 0.5041 | 0.3351 |
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+ | 5.1776 | 6.92 | 90 | 4.8584 | 0.5041 | 0.3351 |
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+ | 5.0097 | 7.69 | 100 | 4.6328 | 0.5041 | 0.3351 |
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+ | 4.7851 | 8.46 | 110 | 4.4098 | 0.5041 | 0.3351 |
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+ | 4.6801 | 9.23 | 120 | 4.2064 | 0.5041 | 0.3351 |
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+ | 4.4144 | 10.0 | 130 | 3.9980 | 0.5041 | 0.3351 |
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+ | 4.1631 | 10.77 | 140 | 3.7914 | 0.5041 | 0.3351 |
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+ | 4.0093 | 11.54 | 150 | 3.5793 | 0.5041 | 0.3351 |
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+ | 3.7803 | 12.31 | 160 | 3.3708 | 0.5041 | 0.3351 |
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+ | 3.645 | 13.08 | 170 | 3.1635 | 0.5041 | 0.3351 |
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+ | 3.3334 | 13.85 | 180 | 2.9564 | 0.5041 | 0.3351 |
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+ | 3.0942 | 14.62 | 190 | 2.7570 | 0.5041 | 0.3351 |
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+ | 3.0844 | 15.38 | 200 | 2.5619 | 0.5041 | 0.3351 |
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+ | 2.749 | 16.15 | 210 | 2.3733 | 0.5041 | 0.3351 |
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+ | 2.5448 | 16.92 | 220 | 2.1896 | 0.5041 | 0.3351 |
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+ | 2.3636 | 17.69 | 230 | 2.0160 | 0.5041 | 0.3351 |
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+ | 2.1303 | 18.46 | 240 | 1.8579 | 0.5041 | 0.3351 |
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+ | 2.1702 | 19.23 | 250 | 1.7136 | 0.5041 | 0.3351 |
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+ | 1.8911 | 20.0 | 260 | 1.5809 | 0.5041 | 0.3351 |
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+ | 1.7695 | 20.77 | 270 | 1.4511 | 0.5041 | 0.3351 |
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+ | 1.5466 | 21.54 | 280 | 1.3433 | 0.5041 | 0.3351 |
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+ | 1.4228 | 22.31 | 290 | 1.2479 | 0.5041 | 0.3351 |
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+ | 1.4089 | 23.08 | 300 | 1.1632 | 0.5041 | 0.3351 |
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+ | 1.2252 | 23.85 | 310 | 1.0900 | 0.5041 | 0.3351 |
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+ | 1.2236 | 24.62 | 320 | 1.0268 | 0.5041 | 0.3351 |
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+ | 1.0727 | 25.38 | 330 | 0.9742 | 0.5041 | 0.3351 |
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+ | 1.0036 | 26.15 | 340 | 0.9273 | 0.5041 | 0.3351 |
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+ | 0.95 | 26.92 | 350 | 0.8892 | 0.5041 | 0.3351 |
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+ | 0.9304 | 27.69 | 360 | 0.8592 | 0.5041 | 0.3351 |
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+ | 0.9426 | 28.46 | 370 | 0.8355 | 0.5041 | 0.3351 |
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+ | 0.8967 | 29.23 | 380 | 0.8136 | 0.5041 | 0.3351 |
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+ | 0.862 | 30.0 | 390 | 0.7942 | 0.5041 | 0.3351 |
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+ | 0.8609 | 30.77 | 400 | 0.7799 | 0.5041 | 0.3351 |
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+ | 0.8013 | 31.54 | 410 | 0.7667 | 0.5041 | 0.3351 |
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+ | 0.7845 | 32.31 | 420 | 0.7572 | 0.5041 | 0.3351 |
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+ | 0.77 | 33.08 | 430 | 0.7425 | 0.5041 | 0.3351 |
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+ | 0.7952 | 33.85 | 440 | 0.7309 | 0.5041 | 0.3351 |
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+ | 0.7433 | 34.62 | 450 | 0.7119 | 0.7046 | 0.7036 |
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+ | 0.7791 | 35.38 | 460 | 0.7002 | 0.7019 | 0.6992 |
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+ | 0.7409 | 36.15 | 470 | 0.6932 | 0.7073 | 0.7029 |
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+ | 0.7233 | 36.92 | 480 | 0.6887 | 0.6911 | 0.6904 |
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+ | 0.716 | 37.69 | 490 | 0.6820 | 0.6992 | 0.6986 |
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+ | 0.6994 | 38.46 | 500 | 0.6821 | 0.6883 | 0.6881 |
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+ | 0.6899 | 39.23 | 510 | 0.6701 | 0.6938 | 0.6930 |
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+ | 0.7014 | 40.0 | 520 | 0.6683 | 0.6965 | 0.6965 |
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+ | 0.7252 | 40.77 | 530 | 0.6632 | 0.7019 | 0.7011 |
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+ | 0.6763 | 41.54 | 540 | 0.6650 | 0.6883 | 0.6882 |
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+ | 0.7115 | 42.31 | 550 | 0.6615 | 0.6829 | 0.6829 |
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+ | 0.6703 | 43.08 | 560 | 0.6628 | 0.6911 | 0.6908 |
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+ | 0.6757 | 43.85 | 570 | 0.6626 | 0.6938 | 0.6935 |
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+ | 0.6672 | 44.62 | 580 | 0.6545 | 0.7046 | 0.7043 |
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+ | 0.6518 | 45.38 | 590 | 0.6559 | 0.6965 | 0.6965 |
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+ | 0.6661 | 46.15 | 600 | 0.6610 | 0.6802 | 0.6800 |
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+ | 0.679 | 46.92 | 610 | 0.6580 | 0.6856 | 0.6856 |
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+ | 0.7079 | 47.69 | 620 | 0.6558 | 0.6911 | 0.6911 |
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+ | 0.7103 | 48.46 | 630 | 0.6536 | 0.6992 | 0.6992 |
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+ | 0.6613 | 49.23 | 640 | 0.6536 | 0.6965 | 0.6965 |
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+ | 0.6326 | 50.0 | 650 | 0.6541 | 0.6938 | 0.6938 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.33.0
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+ - Pytorch 2.0.0
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+ - Datasets 2.1.0
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+ - Tokenizers 0.13.3