safetensors t5 region:us
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# Model Card for t5_small Summarization Model

## Model Details
It is text summarization model from pretrained t5_samll model.

## Training Data
The model is trained with 1percent of cnn-dailymail datas.

## Training Procedure
### First : load pretrained t5_small model and tokenizer.
### Second : make cnn dataset from basic dataset code.
### Third : train the model with DataCollatorForSeq2Seq

### training_args = Seq2SeqTrainingArguments(output_dir="./results",
    eval_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=4,
    per_device_eval_batch_size=4,
    warmup_steps=500,
    weight_decay=0.01,
    save_total_limit=2,
    num_train_epochs=1,
    fp16=True,
    predict_with_generate=True
)


## How to Use
it can be used when you want to summarize some long texts.

## Evaluation
eval_rouge1: 24.34
eval_rouge2: 9.09
eval_rougeL: 19.72
eval_bleu1: 39.95
eval_bleu2: 15.82
eval_bleu4: 6.22
## Limitations

## Ethical Considerations
This model can be exploited some wrong issues, so you must use model in appropriate situations
