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callmesan
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vakyansh-wav2vec2-bengali-bnm-200-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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+ base_model: Harveenchadha/vakyansh-wav2vec2-bengali-bnm-200
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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-bengali-bnm-200-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-bengali-bnm-200-audio-abuse-feature
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+
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+ This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-bengali-bnm-200](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-bengali-bnm-200) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.8024
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+ - Accuracy: 0.6459
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+ - Macro F1-score: 0.6339
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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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+ | 6.7346 | 0.77 | 10 | 6.7291 | 0.0 | 0.0 |
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+ | 6.6926 | 1.54 | 20 | 6.6304 | 0.0027 | 0.0004 |
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+ | 6.5587 | 2.31 | 30 | 6.4243 | 0.5730 | 0.2830 |
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+ | 6.3449 | 3.08 | 40 | 6.1051 | 0.5649 | 0.5571 |
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+ | 6.1232 | 3.85 | 50 | 5.7862 | 0.4216 | 0.3430 |
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+ | 5.8191 | 4.62 | 60 | 5.5131 | 0.4027 | 0.2908 |
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+ | 5.592 | 5.38 | 70 | 5.2719 | 0.5216 | 0.5022 |
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+ | 5.3414 | 6.15 | 80 | 5.0558 | 0.6189 | 0.6186 |
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+ | 5.1331 | 6.92 | 90 | 4.8552 | 0.6865 | 0.6852 |
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+ | 4.98 | 7.69 | 100 | 4.6603 | 0.6568 | 0.6567 |
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+ | 4.7844 | 8.46 | 110 | 4.4634 | 0.6703 | 0.6702 |
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+ | 4.7028 | 9.23 | 120 | 4.2715 | 0.6568 | 0.6567 |
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+ | 4.4476 | 10.0 | 130 | 4.0733 | 0.6297 | 0.6280 |
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+ | 4.2098 | 10.77 | 140 | 3.8749 | 0.6108 | 0.6041 |
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+ | 4.0715 | 11.54 | 150 | 3.6803 | 0.5027 | 0.4564 |
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+ | 3.8545 | 12.31 | 160 | 3.4603 | 0.6649 | 0.6648 |
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+ | 3.708 | 13.08 | 170 | 3.2559 | 0.6541 | 0.6534 |
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+ | 3.4318 | 13.85 | 180 | 3.0493 | 0.6676 | 0.6675 |
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+ | 3.1874 | 14.62 | 190 | 2.8456 | 0.6838 | 0.6837 |
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+ | 3.1887 | 15.38 | 200 | 2.6625 | 0.5595 | 0.5384 |
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+ | 2.8359 | 16.15 | 210 | 2.4679 | 0.5757 | 0.5604 |
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+ | 2.6265 | 16.92 | 220 | 2.2662 | 0.6892 | 0.6841 |
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+ | 2.4536 | 17.69 | 230 | 2.0843 | 0.6649 | 0.6644 |
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+ | 2.2288 | 18.46 | 240 | 1.9218 | 0.6459 | 0.6431 |
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+ | 2.2955 | 19.23 | 250 | 1.7633 | 0.6595 | 0.6578 |
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+ | 1.9739 | 20.0 | 260 | 1.6105 | 0.6730 | 0.6671 |
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+ | 1.8575 | 20.77 | 270 | 1.4855 | 0.6378 | 0.6351 |
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+ | 1.607 | 21.54 | 280 | 1.3582 | 0.6649 | 0.6646 |
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+ | 1.4831 | 22.31 | 290 | 1.2425 | 0.6676 | 0.6646 |
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+ | 1.4484 | 23.08 | 300 | 1.1522 | 0.6703 | 0.6660 |
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+ | 1.2517 | 23.85 | 310 | 1.0688 | 0.6595 | 0.6554 |
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+ | 1.2793 | 24.62 | 320 | 1.0006 | 0.6541 | 0.6523 |
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+ | 1.0722 | 25.38 | 330 | 0.9486 | 0.6568 | 0.6543 |
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+ | 0.9888 | 26.15 | 340 | 0.9292 | 0.6135 | 0.6135 |
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+ | 0.9134 | 26.92 | 350 | 0.8580 | 0.6514 | 0.6492 |
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+ | 0.9208 | 27.69 | 360 | 0.8352 | 0.6649 | 0.6646 |
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+ | 0.966 | 28.46 | 370 | 0.8220 | 0.6162 | 0.6160 |
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+ | 0.8746 | 29.23 | 380 | 0.8064 | 0.6568 | 0.6420 |
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+ | 0.8619 | 30.0 | 390 | 0.7856 | 0.6405 | 0.5942 |
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+ | 0.841 | 30.77 | 400 | 0.7612 | 0.6459 | 0.6020 |
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+ | 0.7629 | 31.54 | 410 | 0.7441 | 0.6459 | 0.6434 |
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+ | 0.6736 | 32.31 | 420 | 0.7610 | 0.6568 | 0.6562 |
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+ | 0.6579 | 33.08 | 430 | 0.7624 | 0.6514 | 0.6456 |
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+ | 0.7514 | 33.85 | 440 | 0.7374 | 0.6649 | 0.6467 |
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+ | 0.6579 | 34.62 | 450 | 0.7503 | 0.6541 | 0.6471 |
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+ | 0.6864 | 35.38 | 460 | 0.8286 | 0.5892 | 0.5889 |
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+ | 0.6863 | 36.15 | 470 | 0.7393 | 0.6541 | 0.6396 |
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+ | 0.6224 | 36.92 | 480 | 0.7427 | 0.6541 | 0.6507 |
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+ | 0.6255 | 37.69 | 490 | 0.7495 | 0.6405 | 0.6268 |
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+ | 0.5295 | 38.46 | 500 | 0.7787 | 0.6486 | 0.6385 |
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+ | 0.5549 | 39.23 | 510 | 0.7909 | 0.6378 | 0.6360 |
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+ | 0.5752 | 40.0 | 520 | 0.7631 | 0.6459 | 0.6361 |
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+ | 0.616 | 40.77 | 530 | 0.7636 | 0.6432 | 0.6390 |
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+ | 0.5038 | 41.54 | 540 | 0.7847 | 0.6514 | 0.6372 |
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+ | 0.5935 | 42.31 | 550 | 0.7837 | 0.6595 | 0.6461 |
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+ | 0.5453 | 43.08 | 560 | 0.7804 | 0.6405 | 0.6330 |
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+ | 0.5378 | 43.85 | 570 | 0.7928 | 0.6514 | 0.6338 |
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+ | 0.4852 | 44.62 | 580 | 0.8249 | 0.6324 | 0.6285 |
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+ | 0.5198 | 45.38 | 590 | 0.8065 | 0.6459 | 0.6186 |
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+ | 0.5067 | 46.15 | 600 | 0.8210 | 0.6162 | 0.6107 |
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+ | 0.5533 | 46.92 | 610 | 0.8053 | 0.6432 | 0.6300 |
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+ | 0.6282 | 47.69 | 620 | 0.7970 | 0.6459 | 0.6316 |
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+ | 0.5617 | 48.46 | 630 | 0.8095 | 0.6243 | 0.6165 |
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+ | 0.5016 | 49.23 | 640 | 0.8038 | 0.6378 | 0.6274 |
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+ | 0.467 | 50.0 | 650 | 0.8024 | 0.6459 | 0.6339 |
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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