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---
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 |