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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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--- |
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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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# DL_Audio_Hatespeech_ast_trainer_push |
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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: 0.6336 |
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- Accuracy: 0.6431 |
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- Recall: 0.7452 |
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- Precision: 0.6237 |
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- F1: 0.6790 |
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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: 5 |
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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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| 0.6432 | 0.9987 | 387 | 0.6686 | 0.5992 | 0.6580 | 0.5944 | 0.6245 | |
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| 0.6414 | 2.0 | 775 | 0.6336 | 0.6431 | 0.7452 | 0.6237 | 0.6790 | |
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| 0.6079 | 2.9987 | 1162 | 0.6505 | 0.6328 | 0.5783 | 0.6561 | 0.6148 | |
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| 0.5088 | 4.0 | 1550 | 0.7122 | 0.6176 | 0.6624 | 0.6136 | 0.6371 | |
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| 0.3005 | 4.9935 | 1935 | 0.9250 | 0.6099 | 0.6038 | 0.6176 | 0.6106 | |
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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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