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---
license: mit
tags:
- generated_from_trainer
datasets:
- hate_speech18
widget:
- text: "'ok, so do we need to kill them too or are the slavs okay ? for some reason whenever i hear the word slav , the word slobber comes to mind and i picture a slobbering half breed creature like the humpback of notre dame or Igor haha"
metrics:
- accuracy
model-index:
- name: deberta-v3-small-hate-speech
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: hate_speech18
type: hate_speech18
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.916058394160584
---
<!-- 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. -->
# DeBERTa v3 small fine-tuned on hate_speech18 dataset for Hate Speech Detection
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the hate_speech18 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2922
- Accuracy: 0.9161
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4147 | 1.0 | 650 | 0.3910 | 0.8832 |
| 0.2975 | 2.0 | 1300 | 0.2922 | 0.9161 |
| 0.2575 | 3.0 | 1950 | 0.3555 | 0.9051 |
| 0.1553 | 4.0 | 2600 | 0.4263 | 0.9124 |
| 0.1267 | 5.0 | 3250 | 0.4238 | 0.9161 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
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