Inseol/t5-small-custom

Model Card for t5_small Summarization Model

Model Details

This model is fine-tuned version of t5-small Model for Text Summarization tasks.

Training Data

The model was trained on the CNN/Daily mail that consist two type of data(article, highlights)

Training Procedure

• Learning Rate: 2e-5
• Epochs: 3
• Batch Size: 4

How to Use

'''python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline tokenizer = AutoTokenizer.from_pretrained('t5-small') model = AutoModelForSeq2SeqLM.from_pretrained('t5-small') summarizer = pipeline("summarization", model = model, tokenizer = tokenizer)

text = "your sentences"

summary = summarizer(text, max_length = 150, min_length = 30, do_sample=False)

print(summary[0]['summary_text']) '''

Evaluation

• ROUGE1: 0.33
• ROUGE2: 0.30
• ROUGEL: 0.33
• BLEU1: 60.00
• BLEU2: 55.56
• BLEU4: 42.86

Limitations

The model may generate biased or inappropriate content due to the nature of the training data. It is recommended to use the model with caution and apply necessary filters.

Ethical Considerations

• Bias: The model may inherit biases present in the training data.
• Misuse: The model can be misused to generate misleading or harmful content.