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If you want to use the model you should try a newer fine-tuned FLAN-T5 version philschmid/flan-t5-base-samsum out socring the BART version with +6 on ROGUE1 achieving 47.24.

TRY philschmid/flan-t5-base-samsum

This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.

For more information look at:


    "dataset_name": "samsum",
    "do_eval": true,
    "do_predict": true,
    "do_train": true,
    "fp16": true,
    "learning_rate": 5e-05,
    "model_name_or_path": "facebook/bart-large-cnn",
    "num_train_epochs": 3,
    "output_dir": "/opt/ml/model",
    "per_device_eval_batch_size": 4,
    "per_device_train_batch_size": 4,
    "predict_with_generate": true,
    "seed": 7


from transformers import pipeline
summarizer = pipeline("summarization", model="philschmid/bart-large-cnn-samsum")

conversation = '''Jeff: Can I train a πŸ€— Transformers model on Amazon SageMaker? 
Philipp: Sure you can use the new Hugging Face Deep Learning Container. 
Jeff: ok.
Jeff: and how can I get started? 
Jeff: where can I find documentation? 
Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face                                           


key value
eval_rouge1 42.621
eval_rouge2 21.9825
eval_rougeL 33.034
eval_rougeLsum 39.6783
test_rouge1 41.3174
test_rouge2 20.8716
test_rougeL 32.1337
test_rougeLsum 38.4149
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