---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# hatespeech_wav2vec2

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/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