## `bart-large-cnn-samsum` > If you want to use the model you should try a newer fine-tuned FLAN-T5 version [philschmid/flan-t5-base-samsum](https://huggingface.co/philschmid/flan-t5-base-samsum) out socring the BART version with `+6` on `ROGUE1` achieving `47.24`. # TRY [philschmid/flan-t5-base-samsum](https://huggingface.co/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: - [🤗 Transformers Documentation: Amazon SageMaker](https://huggingface.co/transformers/sagemaker.html) - [Example Notebooks](https://github.com/huggingface/notebooks/tree/master/sagemaker) - [Amazon SageMaker documentation for Hugging Face](https://docs.aws.amazon.com/sagemaker/latest/dg/hugging-face.html) - [Python SDK SageMaker documentation for Hugging Face](https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/index.html) - [Deep Learning Container](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#huggingface-training-containers) ## Hyperparameters ```json { "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 } ``` ## Usage ```python from transformers import pipeline summarizer = pipeline("summarization", model="philschmid/bart-large-cnn-samsum") conversation = '''Jeff: Can I train a 🤗 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 ''' summarizer(conversation) ``` ## Results | 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 |