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callmesan
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vakyansh-wav2vec2-tamil-tam-250-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-tamil-tam-250
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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-tamil-tam-250-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-tamil-tam-250-audio-abuse-feature
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+
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+ This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-tamil-tam-250](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-tamil-tam-250) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6061
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+ - Accuracy: 0.7412
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+ - Macro F1-score: 0.6531
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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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+ | 6.7458 | 0.77 | 10 | 6.7472 | 0.0 | 0.0 |
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+ | 6.7056 | 1.54 | 20 | 6.6488 | 0.0 | 0.0 |
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+ | 6.6158 | 2.31 | 30 | 6.5180 | 0.6307 | 0.0917 |
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+ | 6.4651 | 3.08 | 40 | 6.2887 | 0.7143 | 0.4167 |
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+ | 6.2508 | 3.85 | 50 | 5.9094 | 0.7197 | 0.4185 |
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+ | 5.8959 | 4.62 | 60 | 5.5362 | 0.7197 | 0.4185 |
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+ | 5.6179 | 5.38 | 70 | 5.2347 | 0.7197 | 0.4185 |
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+ | 5.3048 | 6.15 | 80 | 4.9823 | 0.7197 | 0.4185 |
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+ | 5.0858 | 6.92 | 90 | 4.7555 | 0.7197 | 0.4185 |
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+ | 4.9195 | 7.69 | 100 | 4.5424 | 0.7197 | 0.4185 |
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+ | 4.6747 | 8.46 | 110 | 4.3265 | 0.7197 | 0.4185 |
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+ | 4.5861 | 9.23 | 120 | 4.1193 | 0.7197 | 0.4185 |
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+ | 4.3397 | 10.0 | 130 | 3.9070 | 0.7197 | 0.4185 |
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+ | 4.0926 | 10.77 | 140 | 3.6954 | 0.7197 | 0.4185 |
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+ | 3.8859 | 11.54 | 150 | 3.4822 | 0.7197 | 0.4185 |
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+ | 3.7254 | 12.31 | 160 | 3.2711 | 0.7197 | 0.4185 |
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+ | 3.5303 | 13.08 | 170 | 3.0599 | 0.7197 | 0.4185 |
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+ | 3.2531 | 13.85 | 180 | 2.8502 | 0.7197 | 0.4185 |
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+ | 3.0184 | 14.62 | 190 | 2.6448 | 0.7197 | 0.4185 |
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+ | 3.0006 | 15.38 | 200 | 2.4472 | 0.7197 | 0.4185 |
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+ | 2.6674 | 16.15 | 210 | 2.2526 | 0.7197 | 0.4185 |
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+ | 2.4455 | 16.92 | 220 | 2.0649 | 0.7197 | 0.4185 |
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+ | 2.2702 | 17.69 | 230 | 1.8883 | 0.7197 | 0.4185 |
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+ | 2.0536 | 18.46 | 240 | 1.7233 | 0.7197 | 0.4185 |
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+ | 2.0643 | 19.23 | 250 | 1.5730 | 0.7197 | 0.4185 |
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+ | 1.8006 | 20.0 | 260 | 1.4368 | 0.7197 | 0.4185 |
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+ | 1.6975 | 20.77 | 270 | 1.3112 | 0.7197 | 0.4185 |
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+ | 1.4407 | 21.54 | 280 | 1.2015 | 0.7197 | 0.4185 |
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+ | 1.2971 | 22.31 | 290 | 1.1050 | 0.7197 | 0.4185 |
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+ | 1.3202 | 23.08 | 300 | 1.0219 | 0.7197 | 0.4185 |
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+ | 1.1292 | 23.85 | 310 | 0.9490 | 0.7197 | 0.4185 |
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+ | 1.1055 | 24.62 | 320 | 0.8879 | 0.7197 | 0.4185 |
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+ | 0.9817 | 25.38 | 330 | 0.8366 | 0.7197 | 0.4185 |
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+ | 0.9296 | 26.15 | 340 | 0.7906 | 0.7197 | 0.4185 |
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+ | 0.8306 | 26.92 | 350 | 0.7506 | 0.7197 | 0.4185 |
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+ | 0.8303 | 27.69 | 360 | 0.7171 | 0.7197 | 0.4185 |
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+ | 0.8421 | 28.46 | 370 | 0.6953 | 0.7197 | 0.4185 |
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+ | 0.7964 | 29.23 | 380 | 0.6650 | 0.7197 | 0.4185 |
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+ | 0.7528 | 30.0 | 390 | 0.6470 | 0.7197 | 0.4185 |
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+ | 0.7305 | 30.77 | 400 | 0.6345 | 0.7197 | 0.4185 |
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+ | 0.6702 | 31.54 | 410 | 0.6163 | 0.7385 | 0.4937 |
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+ | 0.6416 | 32.31 | 420 | 0.6118 | 0.7547 | 0.5507 |
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+ | 0.608 | 33.08 | 430 | 0.6086 | 0.7547 | 0.5507 |
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+ | 0.6659 | 33.85 | 440 | 0.5981 | 0.7574 | 0.5949 |
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+ | 0.5839 | 34.62 | 450 | 0.6068 | 0.7547 | 0.6570 |
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+ | 0.6167 | 35.38 | 460 | 0.5894 | 0.7763 | 0.6479 |
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+ | 0.5991 | 36.15 | 470 | 0.5947 | 0.7412 | 0.6531 |
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+ | 0.5839 | 36.92 | 480 | 0.5938 | 0.7574 | 0.6771 |
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+ | 0.5533 | 37.69 | 490 | 0.5922 | 0.7520 | 0.6399 |
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+ | 0.4998 | 38.46 | 500 | 0.6203 | 0.7358 | 0.6625 |
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+ | 0.5508 | 39.23 | 510 | 0.5865 | 0.7493 | 0.6278 |
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+ | 0.5159 | 40.0 | 520 | 0.5963 | 0.7385 | 0.6670 |
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+ | 0.5344 | 40.77 | 530 | 0.5946 | 0.7439 | 0.6420 |
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+ | 0.5039 | 41.54 | 540 | 0.5979 | 0.7466 | 0.6526 |
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+ | 0.5456 | 42.31 | 550 | 0.5999 | 0.7358 | 0.6707 |
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+ | 0.4822 | 43.08 | 560 | 0.5845 | 0.7493 | 0.6437 |
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+ | 0.4864 | 43.85 | 570 | 0.6035 | 0.7439 | 0.6779 |
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+ | 0.4623 | 44.62 | 580 | 0.5961 | 0.7520 | 0.6519 |
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+ | 0.475 | 45.38 | 590 | 0.6066 | 0.7439 | 0.6651 |
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+ | 0.4887 | 46.15 | 600 | 0.6014 | 0.7466 | 0.6603 |
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+ | 0.506 | 46.92 | 610 | 0.6012 | 0.7412 | 0.6604 |
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+ | 0.5296 | 47.69 | 620 | 0.5986 | 0.7439 | 0.6503 |
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+ | 0.5255 | 48.46 | 630 | 0.6003 | 0.7439 | 0.6503 |
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+ | 0.4667 | 49.23 | 640 | 0.6038 | 0.7466 | 0.6553 |
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+ | 0.4334 | 50.0 | 650 | 0.6061 | 0.7412 | 0.6531 |
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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