jun0217/t5-small-custom

Model Card for t5_small Summarization Model

Model Details

This model is a fine-tuned version of t5_small on the CNN/Daily Mail dataset for summarization tasks.

Training Data

The model was trained on the CNN/Daily Mail dataset.

Training Procedure

• Learning Rate: 5e-5
• Epochs: 3
• Batch Size: 16

How to Use

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("hskang/cnn_dailymail_t5_small")
model = AutoModelForSeq2SeqLM.from_pretrained("hskang/cnn_dailymail_t5_small")

input_text = "upstage tutorial text summarization code"
inputs = tokenizer.encode(input_text, return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Evaluation

• ROUGE-1: 23.45
• ROUGE-2: 7.89
• ROUGE-L: 21.34
• BLEU: 13.56

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. Copyright and License This model is licensed under the MIT License.