metadata

license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - recall
  - precision
  - f1
model-index:
  - name: DL_Audio_Hatespeech_ast_trainer_push
    results: []
widget:
  - src: example_hate_speech.wav
    example_title: Hate Speech Example
  - src: example_non_hate.wav
    example_title: Non-Hate Speech Example

hatespeech_ast

This model is a fine-tuned version of MIT/ast-finetuned-audioset-10-10-0.4593 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6306
Accuracy: 0.6486
Recall: 0.8368
Precision: 0.6136
F1: 0.7080

And the following results on the test set:

Loss: 0.6441
Accuracy: 0.6318
Recall: 0.8191
Precision: 0.6001
F1: 0.6927

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Recall	Precision	F1
0.753	1.0	310	0.6793	0.5559	0.2258	0.6968	0.3411
0.6598	2.0	620	0.6447	0.6265	0.7575	0.6066	0.6737
0.6374	3.0	930	0.6306	0.6486	0.8368	0.6136	0.7080
0.5586	4.0	1240	0.7678	0.6091	0.9144	0.5727	0.7043
0.4008	5.0	1550	0.8134	0.6212	0.5515	0.6511	0.5972
0.2072	6.0	1860	1.0746	0.6265	0.7448	0.6088	0.6700
0.0904	7.0	2170	2.0297	0.6273	0.6878	0.6209	0.6526
0.0203	8.0	2480	3.0627	0.6236	0.6307	0.6302	0.6305
0.0244	9.0	2790	3.2017	0.6297	0.7013	0.6206	0.6585
0.0	10.0	3100	3.2659	0.6313	0.6331	0.6392	0.6361

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cu121
Datasets 2.3.2
Tokenizers 0.19.1