| # 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 |