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