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# Model Detail Information
### 1. Overview
This model is trained to detect the presence of harmful expressions in Korean sentences.<br>
It performs binary classification to determine whether a given sentence contains hateful expressions or is a general, non-hateful sentence.<br>
This model is designed for the AI task of 'text classification', using the 'TTA-DQA/hate_sentence' dataset.<br>
The classification labels are:
- "0": "no_hate"
- "1": "hate"
### 2. Training Information
- Base Model: KcElectra (a pre-trained Korean language model based on Electra)
- Source: beomi/KcELECTRA-base-v2022(https://huggingface.co/beomi/KcELECTRA-base-v2022)
- Model Type: Casual Language Model
- Pre-training (Korean): Approximately 17GB (over 180 million sentences)
- Fine-tuning (hate dataset): Approximately 22.3MB(TTA-DQA/hate_sentence)
- Learning Rate: 5e-6
- Weight Decay: 0.01
- Epochs: 20
- Batch Size: 16
- Data Loader Workers: 2
- Tokenizer: BertWordPieceTokenizer
- Model Size: Approximately 512MB
### 3. Requirements
To use this model, ensure the following dependencies are installed:
- pytorch ~= 1.8.0
- transformers ~= 4.11.3
- emoji ~= 0.6.0
- soynlp ~= 0.0.493
### 4. Quick Start
- python
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("TTA-DQA/HateDetection-KcElectra-FineTuning")
model = AutoModel.from_pretrained("TTA-DQA/HateDetection-KcElectra-FineTuning")
```
### 5. Citation
- This model was developed as part of the Quality Validation Project for Super-Giant AI Training Data (305-2100-2131, 2024 Quality Validation for Super-Giant AI Training).
### 6. Bias, Risks, and Limitations
- The determination of harmful expressions may vary depending on language, culture, application context, and personal perspectives.
- Results may reflect biases or lead to controversy due to the subjective nature of evaluating harmful content.
- This model's outputs should not be considered as definitive standards for identifying harmful expressions.
# Results
- type : binary classification(text-classification)
- f1-score : 0.9928
- accuracy : 0.9928 |