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# Model Card for t5_small Summarization Model

## Model Details
- **Model Name:** t5-small
- **Model Type:** Text Summarization
- **Architecture:** Transformer-based model
- **Number of Parameters:** Approximately 60 million

## Training Data
- **Dataset:** CNN/Daily Mail
- **Data Source:** [Hugging Face Datasets](https://huggingface.co/docs/datasets/)
- **Training Size:** X samples (X는 실제 훈련 데이터 수로 교체)
- **Data Characteristics:** The dataset consists of news articles and their corresponding summaries.

## Training Procedure
- **Training Framework:** Hugging Face Transformers
- **Batch Size:** 4
- **Learning Rate:** 2e-5
- **Number of Epochs:** 1
- **Evaluation Metrics:** ROUGE, BLEU

## How to Use
- **Dependencies:**
  - Install required libraries: `transformers`, `datasets`
- **Example Usage:**
  ```python
  from transformers import pipeline
  summarizer = pipeline("summarization", model="your_model_path")
  summary = summarizer("Your input text here.")

