---

language: en tags:

• sagemaker
• bart
• summarization datasets:
• samsum widget:
• text: "Jeff: Can I train a \U0001F917 Transformers model on Amazon SageMaker? \n
  Philipp: Sure you can use the new Hugging Face Deep Learning Container. \nJeff:
  \ ok.\nJeff: and how can I get started? \nJeff: where can I find documentation?
  \ \nPhilipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face\n" model-index:
• name: bart-large-cnn-samsum results:
  • task: name: Abstractive Text Summarization type: abstractive-text-summarization dataset: name: 'SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization' type: samsum metrics:
    • name: Validation ROGUE-1 type: rogue-1 value: 42.621
    • name: Validation ROGUE-2 type: rogue-2 value: 21.9825
    • name: Validation ROGUE-L type: rogue-l value: 33.034
    • name: Test ROGUE-1 type: rogue-1 value: 41.3174
    • name: Test ROGUE-2 type: rogue-2 value: 20.8716
    • name: Test ROGUE-L type: rogue-l value: 32.1337
  • task: type: summarization name: Summarization dataset: name: samsum type: samsum config: samsum split: test metrics:
    • name: ROUGE-1 type: rouge value: 41.3282 verified: true
    • name: ROUGE-2 type: rouge value: 20.8755 verified: true
    • name: ROUGE-L type: rouge value: 32.1353 verified: true
    • name: ROUGE-LSUM type: rouge value: 38.401 verified: true
    • name: loss type: loss value: 1.4297215938568115 verified: true
    • name: gen_len type: gen_len value: 60.0757 verified: true

---

bart-large-cnn-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
• Example Notebooks
• Amazon SageMaker documentation for Hugging Face
• Python SDK SageMaker documentation for Hugging Face
• Deep Learning Container

Hyperparameters

{
    "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

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                                           
'''
nlp(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