#
# Model Card for t5_small Summarization Model

## Model Details
- **Model Name**: T5-small Summarization Model
- **Architecture**: T5-small
- **Purpose**: Summarization of news articles from the CNN/DailyMail dataset.

## Training Data
- **Dataset**: CNN/DailyMail dataset (version 3.0.0)

## Training Procedure
- **Learning Rate**: 2e-5
- **Batch Size**: 4 (per device)
- **Epochs**: 3
- **Evaluation**: ROUGE and BLEU scores were used to evaluate the summarization quality.

## How to Use
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Yeop9690/t5-small-cnn-dailymail-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("Yeop9690/t5-small-cnn-dailymail-summarization")
```

## Evaluation
Test Results:
- eval_rouge1: 0.49
- eval_rouge2: 0.30
- eval_rougeL: 0.45
- eval_bleu1: 38.46
- eval_bleu2: 25.00
- eval_bleu4: 15.00

## Limitations
- This model may not perform well on highly technical or domain-specific content.
- The summaries may sometimes miss important context or nuances in the original text.

## Ethical Considerations