Model Card for t5_small Summarization Model

Model Details

This model is a fine-tuned version of T5-small for text summarization tasks.

Training Data

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

Training Procedure

Fine-tuning the pre-trained T5-small model on the CNN/Daily Mail dataset.

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

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model = AutoModelForSeq2SeqLM.from_pretrained("repo_name")
tokenizer = AutoTokenizer.from_pretrained("repo_name")

inputs = tokenizer("input text", return_tensors="pt")
outputs = model.generate(**inputs)
summary = tokenizer.decode(outputs[0], skip_special_tokens=True)

Evaluation

ROUGE, BLEU 'ROUGE-1', 'ROUGE-2', 'ROUGE-L', 'BLEU-1', 'BLEU-2', 'BLEU-4'

Limitations

The model may not perform well on texts.

Ethical Considerations

The model should be used responsibly.