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--- |
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license: bsd-3-clause |
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base_model: MIT/ast-finetuned-audioset-10-10-0.4593 |
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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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- recall |
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- precision |
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- f1 |
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model-index: |
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- name: DL_Audio_Hatespeech_ast_trainer_push |
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results: [] |
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widget: |
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- src: example_hate_speech.wav |
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example_title: Hate Speech Example |
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- src: example_non_hate.wav |
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example_title: Non-Hate Speech Example |
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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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# hatespeech_ast |
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This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6306 |
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- Accuracy: 0.6486 |
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- Recall: 0.8368 |
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- Precision: 0.6136 |
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- F1: 0.7080 |
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And the following results on the test set: |
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- Loss: 0.6441 |
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- Accuracy: 0.6318 |
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- Recall: 0.8191 |
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- Precision: 0.6001 |
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- F1: 0.6927 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-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: 2 |
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- total_train_batch_size: 32 |
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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: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.753 | 1.0 | 310 | 0.6793 | 0.5559 | 0.2258 | 0.6968 | 0.3411 | |
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| 0.6598 | 2.0 | 620 | 0.6447 | 0.6265 | 0.7575 | 0.6066 | 0.6737 | |
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| 0.6374 | 3.0 | 930 | 0.6306 | 0.6486 | 0.8368 | 0.6136 | 0.7080 | |
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| 0.5586 | 4.0 | 1240 | 0.7678 | 0.6091 | 0.9144 | 0.5727 | 0.7043 | |
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| 0.4008 | 5.0 | 1550 | 0.8134 | 0.6212 | 0.5515 | 0.6511 | 0.5972 | |
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| 0.2072 | 6.0 | 1860 | 1.0746 | 0.6265 | 0.7448 | 0.6088 | 0.6700 | |
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| 0.0904 | 7.0 | 2170 | 2.0297 | 0.6273 | 0.6878 | 0.6209 | 0.6526 | |
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| 0.0203 | 8.0 | 2480 | 3.0627 | 0.6236 | 0.6307 | 0.6302 | 0.6305 | |
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| 0.0244 | 9.0 | 2790 | 3.2017 | 0.6297 | 0.7013 | 0.6206 | 0.6585 | |
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| 0.0 | 10.0 | 3100 | 3.2659 | 0.6313 | 0.6331 | 0.6392 | 0.6361 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.3.2 |
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- Tokenizers 0.19.1 |
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