metadata

license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - recall
  - precision
  - f1
model-index:
  - name: DL-Project/hatespeech_wav2vec2
    results: []
datasets:
  - DL-Project/DL_Audio_Hatespeech_Dataset
language:
  - en
widget:
  - src: example_hate.wav
    example_title: Hate Speech Example
  - src: example_non_hate.wav
    example_title: Non-Hate Speech Example

hatespeech_wav2vec2

This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6562
Accuracy: 0.6216
Recall: 0.7853
Precision: 0.5990
F1: 0.6796

It achieves the following results on the test set:

Loss: 0.6597
Accuracy: 0.6192
Recall: 0.7822
Precision: 0.5944
F1: 0.6755

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: 4e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
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

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Recall	Precision	F1
No log	0.9935	77	0.6871	0.5430	0.9021	0.5311	0.6686
0.6899	2.0	155	0.6779	0.5647	0.9021	0.5448	0.6793
0.6761	2.9935	232	0.6649	0.5934	0.5541	0.6131	0.5821
0.6607	4.0	310	0.6550	0.6289	0.6504	0.6334	0.6417
0.6607	4.9935	387	0.6562	0.6216	0.7853	0.5990	0.6796
0.6403	6.0	465	0.6578	0.6357	0.6969	0.6298	0.6617
0.6129	6.9935	542	0.6623	0.6313	0.7277	0.6184	0.6686
0.6024	8.0	620	0.6745	0.6345	0.7490	0.6174	0.6769
0.5779	8.9935	697	0.6807	0.6406	0.6567	0.6460	0.6513
0.5779	9.9355	770	0.6798	0.6337	0.6993	0.6270	0.6612

Framework versions

Transformers 4.40.2
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1