---
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
---

<!-- 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_ast

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.
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