ruddnjsfk/fundoheydo

-- license: apache-2.0 datasets: - common-pile/caselaw_access_project - skt/kobest_v1 language: - en - ko metrics: - accuracy base_model: - venoliah/huggingfaceTransfer new_version: skt/kogpt2-base-v2 pipeline_tag: text-ranking library_name: transformers tags: - text-generation-inference

Model Card for bonnie/kogpt2-sst2-text-ranking

이 모델은 한국어와 영어를 모두 지원하는 텍스트 랭킹용 트랜스포머 모델입니다.
대화, 문장 분류, 텍스트 분석 등 다양한 자연어 처리 작업에 활용할 수 있어요.

간단한 사용법은 아래와 같습니다:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

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]
- **Model type:** [Conversation Ai]
- **Language(s) (NLP):** [Korean, English]
- **License:** [Apache-2.0]

- **Repository:** [**Repository:** https://huggingface.co/bonnie/kogpt2-sst2-text-ranking]

## Uses

This model is intended for general-purpose natural language understanding and generation.
 Potential use cases include:
Experimenting with conversational AI or chatbot prototypes
Creative writing assistance (e.g., story or poetry generation)
Text summarization and paraphrasing
Automating simple text-based tasks or workflows
Educational and research projects in natural language processing
The model is suitable for hobbyists, researchers, and developers interested in exploring language model capabilities in a variety of domains.

### Direct Use

This model is designed for a broad range of natural language understanding and generation tasks. Intended users include:
Developers seeking to build conversational AI or virtual assistants
Researchers exploring human-AI interaction
Individuals or organizations interested in automating text-based workflows, creative writing, or customer support
The model can be used as-is for prototyping, experimentation, and integration into larger applications. It is suitable for scenarios where advanced, context-aware text processing is required.

### Downstream Use [optional]

The model may also be fine-tuned or incorporated into more complex systems, such as:
Domain-specific chatbots
Productivity tools
Educational platforms
Creative content generators
Organizations can adapt the model for their unique needs, leveraging its flexible architecture for a variety of downstream tasks.


### Out-of-Scope Use

The model should not be used for:
Applications requiring critical decision-making in legal, medical, or financial contexts
Generating or spreading harmful, misleading, or unethical content
Any use cases that may violate privacy or ethical guidelines

## Bias, Risks, and Limitations

Not suitable for commercial or high-stakes applications
May generate inaccurate or biased content
Should not be relied upon for factual, legal, or medical advice


### Recommendations

- 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 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 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  

#### Training Hyperparameters

- 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 depend on the final model configuration and available compute resources.  
More detailed information will be provided after model training is complete.

## Evaluation

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 variety of standard benchmark datasets for natural language understanding, reasoning, and conversational ability.  
Further details will be provided in the evaluation documentation.

 
#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

Evaluation considers:  
- Domain and topic diversity  
- Demographic and linguistic representation  
- Safety and appropriateness of outputs

#### Metrics

Perplexity
Task-specific accuracy/F1 score
Human evaluation for helpfulness, safety, and bias

### Results

Initial results show that the model performs competitively on general language understanding and generation tasks.
 Further evaluation and detailed results will be shared after additional testing.

#### Summary

The model is suitable for experimentation, prototyping, and research, but is not recommended for high-stakes or commercial applications without further validation.

## Model Examination [optional]

Interpretability and analysis tools (such as attention visualization and prompt tracing) are planned to support responsible deployment and further understanding of model behavior.

## Environmental Impact

Carbon emissions for model training are estimated using the Machine Learning Impact calculator:

#### 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.

### Compute Infrastructure

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  

#### 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  

**BibTeX:**

```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}
}