Create readme-eng.md

readme-eng.md

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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 multi-label classification to determine whether a given sentence contains hateful expressions.<br>
+This model is designed for the AI task of 'multi-label text-classification', using the 'TTA-DQA/hate_sentence' dataset.<br>
+
+The classification labels are:
+- 0: 'insult'
+- 1: 'abuse',
+- 2: 'obscenity'
+- 3: 'TVPC' #Threats of violence/promotion of crime
+- 4: 'sexuality'
+- 5: 'age'
+- 6: 'race_region' #race and region
+- 7: 'disabled'
+- 8: 'religion'
+- 9: 'politics'
+- 10: 'job'
+- 11:'no_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 28.9MB (TTA-DQA/hate_sentence)
+- Learning Rate: 5e-6
+- Weight Decay: 0.01
+- Epochs: 30
+- Batch Size: 16
+- Data Loader Workers: 2
+- Tokenizer: BertWordPieceTokenizer
+- Model Size: Approximately 511MB
+
+### 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/Hate-Detection-MultiLabel-KcElectra-FineTuning")
+model = AutoModel.from_pretrained("TTA-DQA/Hate-Detection-MultiLabel-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 : multi-label classification(text-classification)
+- f1-score : 0.8279
+- accuracy : 0.7013