Update README.md

README.md

@@ -1,4 +1,4 @@
----
+--
 license: apache-2.0
 datasets:
 - common-pile/caselaw_access_project
@@ -16,20 +16,38 @@ library_name: transformers
 tags:
 - text-generation-inference
 ---
-# Model Card for Model ID
+# Model Card for bonnie/kogpt2-sst2-text-ranking
 
-This model card serves as a base template for a new experimental language model.
- The model was developed for research and hobby purposes, as part of ongoing exploration in natural language processing.
+이 모델은 한국어와 영어를 모두 지원하는 텍스트 랭킹용 트랜스포머 모델입니다.  
+대화, 문장 분류, 텍스트 분석 등 다양한 자연어 처리 작업에 활용할 수 있어요.  
 
-## Model Details
+간단한 사용법은 아래와 같습니다:
 
-### Model Description
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-This model is a transformer-based language model designed for general-purpose natural language understanding and generation.
- It is intended for experimentation, prototyping, and research in areas such as conversational AI, creative writing, and text analysis.
- The model was created using standard, widely-adopted open-source tools and does not incorporate proprietary or external frameworks.
+model_name = "bonnie/kogpt2-sst2-text-ranking"
+
+inputs = tokenizer(
+    ["gimothy desyo", "hoho, came back again?"],  # 리스트로 묶어야 함
+    return_tensors="pt",
+    padding=True,
+    truncation=True
+)
+
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
+inputs = tokenizer("gimothy desyo","hoho,came back again?" return_tensors="pt", padding=True, truncation=True)
+## Model Details
+
+Model Description / 모델 설명
+This model is a transformer-based language model designed for general-purpose natural language understanding and generation.
+It is intended for experimentation, prototyping, and research in areas such as conversational AI, creative writing, and text analysis.
+The model was created using standard, widely-adopted open-source tools and does not incorporate proprietary or external frameworks.
 
+이 모델은 일반적인 자연어 이해와 생성을 위해 만들어진 트랜스포머 기반 언어 모델입니다.
+대화형 AI, 창작, 텍스트 분석 등 다양한 연구와 실험, 프로토타입 제작에 활용할 수 있습니다.
+표준적인 오픈소스 도구를 사용하여 개발되었으며, 별도의 독점 소프트웨어나 외부 프레임워크는 포함하지 않았습니다.
 
 
 - **Developed by:** [Bonnie]
@@ -37,7 +55,7 @@ This model is a transformer-based language model designed for general-purpose na
 - **Language(s) (NLP):** [Korean, English]
 - **License:** [Apache-2.0]
 
-- **Repository:** [For fun]
+- **Repository:** [**Repository:** https://huggingface.co/bonnie/kogpt2-sst2-text-ranking]
 
 ## Uses
 
@@ -84,55 +102,59 @@ Should not be relied upon for factual, legal, or medical advice
 
 ### Recommendations
 
-Users should not rely on the model for critical decisions in domains such as healthcare, law, or finance.
-All outputs should be reviewed by humans before use in sensitive or public-facing contexts.
-Regular audits are recommended to monitor for bias and inappropriate content.
-Developers should implement safeguards to prevent misuse and clearly communicate limitations to end-users.
+- Users should not rely on the model for critical decisions in sensitive domains such as healthcare, law, or finance.  
+- All outputs should be reviewed by humans before use in sensitive or public-facing contexts.  
+- Regular audits are recommended to monitor for bias and inappropriate content.  
+- Developers should implement safeguards to prevent misuse and clearly communicate the model’s limitations to end-users.
+
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
+*(Further usage examples and documentation will be provided soon.)*
+
 [More Information Needed]
 
 ## Training Details
 
 ### Training Data
 
-The model is trained on a diverse, large-scale collection of publicly available text from a variety of sources and domains. Data was filtered to remove low-quality or inappropriate content and to minimize the inclusion of personally identifiable information.
- A detailed dataset card and further documentation will be provided upon public release.
+The model is trained on a diverse, large-scale collection of publicly available texts from various sources and domains.  
+Data was filtered to remove low-quality or inappropriate content and to minimize the inclusion of personally identifiable information.  
+A detailed dataset card and further documentation will be provided upon public release.
 
 ### Training Procedure
 
-The model is trained using standard supervised learning techniques for language models. The procedure follows best practices for large-scale natural language processing.
+The model was trained using standard supervised learning techniques for language models, following best practices for large-scale natural language processing.
 
 #### Preprocessing [optional]
 
-Deduplication and cleaning of raw text data
-Filtering for quality and appropriateness
-Tokenization and formatting for model input
-
+- Deduplication and cleaning of raw text data  
+- Filtering for quality and appropriateness  
+- Tokenization and formatting for model input  
 
 #### Training Hyperparameters
 
-Training regime: Mixed precision (e.g., fp16 or bf16) for efficiency and scalability
-Batch size, learning rate, optimizer: Set according to established best practices for large language models
-Further details will be provided in technical documentation after training is complete.
+- Training regime: Mixed precision (e.g., fp16 or bf16) for efficiency and scalability  
+- Batch size, learning rate, optimizer: Configured according to established best practices for large language models  
+- Further details will be provided in technical documentation after training completion.
 
 #### Speeds, Sizes, Times [optional]
 
-Training time, throughput, and checkpoint size are dependent on the final model configuration and available compute resources.
-More information will be provided after model training.
+Training time, throughput, and checkpoint size depend on the final model configuration and available compute resources.  
+More detailed information will be provided after model training is complete.
 
 ## Evaluation
 
-he model was evaluated using a selection of publicly available benchmark datasets for natural language processing.
- Specific datasets and details will be provided upon public release.
+The model was evaluated using a selection of publicly available benchmark datasets for natural language processing.  
+Specific datasets and detailed results will be shared upon public release.
 
 ### Testing Data, Factors & Metrics
 
-The model is evaluated on a range of standard benchmark datasets for natural language understanding, reasoning, and conversational ability.
- Details will be available in the evaluation documentation.
+The model is evaluated on a variety of standard benchmark datasets for natural language understanding, reasoning, and conversational ability.  
+Further details will be provided in the evaluation documentation.
+
  
 #### Testing Data
 
@@ -142,10 +164,10 @@ The model is evaluated on a range of standard benchmark datasets for natural lan
 
 #### Factors
 
-Evaluation considers:
-Domain and topic diversity
-Demographic and linguistic representation
-Safety and appropriateness of outputs
+Evaluation considers:  
+- Domain and topic diversity  
+- Demographic and linguistic representation  
+- Safety and appropriateness of outputs
 
 #### Metrics
 
@@ -170,55 +192,51 @@ Interpretability and analysis tools (such as attention visualization and prompt
 
 Carbon emissions for model training are estimated using the Machine Learning Impact calculator:
 
-- **Hardware Type:** [High-performance GPUs (e.g., NVIDIA A100)]
-- **Hours used:** [To be determined]
-- **Cloud Provider:** [To be determined]
-- **Compute Region:** [To be determined]
-- **Carbon Emitted:** [To be determined]
+#### Hardware
+- Multi-GPU clusters (e.g., NVIDIA A100 or equivalent)  
+- High-memory nodes to support large model sizes and batch processing  
+#### Software
+- Python 3.x  
+- PyTorch or TensorFlow (depending on final implementation)  
+- Hugging Face Transformers library (for model management and inference)  
+- Additional open-source libraries for data preprocessing and evaluation
+
+- **Hours used:** [To be determined]  
+- **Cloud Provider:** [To be determined]  
+- **Compute Region:** [To be determined]  
+- **Carbon Emitted:** [To be determined]  
 
 ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-[The model is based on a standard transformer architecture, following widely adopted practices in natural language processing.
- No proprietary or external frameworks are disclosed at this stage.
- The primary objective is to enable advanced, context-aware language understanding and generation.]
+The model is based on a standard transformer architecture, following widely adopted practices in natural language processing.  
+No proprietary or external frameworks are disclosed at this stage.  
+The primary objective is to enable advanced, context-aware language understanding and generation.
 
 ### Compute Infrastructure
 
-[The model is trained and evaluated using high-performance computing resources suitable for large-scale machine learning.]
+The model is trained and evaluated using high-performance computing resources suitable for large-scale machine learning.
 
 #### Hardware
 
-[Multi-GPU clusters (e.g., NVIDIA A100 or equivalent)
-High-memory nodes to support large model sizes and batch processing]
+- Multi-GPU clusters (e.g., NVIDIA A100 or equivalent)  
+- High-memory nodes to support large model sizes and batch processing  
 
 #### Software
 
-[Python 3.x
-PyTorch or TensorFlow (depending on final implementation)
-Hugging Face Transformers library (for model management and inference)
-Additional open-source libraries for data preprocessing and evaluation]
+- Python 3.x  
+- PyTorch or TensorFlow (depending on final implementation)  
+- Hugging Face Transformers library (for model management and inference)  
+- Additional open-source libraries for data preprocessing and evaluation  
 
 **BibTeX:**
 
-[@misc{yourmodel2025,
+```bibtex
+@misc{yourmodel2025,
   title={A Large-Scale Transformer Model for Natural Language Understanding and Generation},
   author={Anonymous},
   year={2025},
   howpublished={\url{https://jainpromp-architecture.com}},
   note={Preliminary release}
-}
-]
-
-**APA:**
-
-[Anonymous. (2025). A Large-Scale Transformer Model for Natural Language Understanding and Generation]
-
-
-## Glossary [optional]
-
-Transformer: A neural network architecture widely used for natural language processing tasks.
-Tokenization: The process of converting text into smaller units (tokens) for model input.
-Perplexity: A metric used to evaluate language model performance; lower values indicate better predictive accuracy.
-Mixed precision: Training using both 16-bit and 32-bit floating point numbers to improve efficiency.
+}