readme: ---
  language: en
  license: apache-2.0
  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\
      \  "
  model-index:
  - name: philschmid/bart-base-samsum
    results:
    - task:
        type: summarization
        name: Summarization
      dataset:
        name: samsum
        type: samsum
        config: samsum
        split: test
      metrics:
      - type: rouge
        value: 45.3438
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        verified: true
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        name: loss
        verified: true
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  ---
  
  ## `bart-base-samsum`
  This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
  
  You can find the notebook [here]() and the referring blog post [here]().
  
  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_train": true,
      "fp16": true,
      "learning_rate": 5e-05,
      "model_name_or_path": "facebook/bart-base",
      "num_train_epochs": 3,
      "output_dir": "/opt/ml/model",
      "per_device_eval_batch_size": 8,
      "per_device_train_batch_size": 8,
      "seed": 7
  }
  ```
  
  ## Train results
  
  | key | value |
  | --- | ----- |
  | epoch | 3 |
  | init_mem_cpu_alloc_delta | 180190 |
  | init_mem_cpu_peaked_delta | 18282 |
  | init_mem_gpu_alloc_delta | 558658048 |
  | init_mem_gpu_peaked_delta | 0 |
  | train_mem_cpu_alloc_delta | 6658519 |
  | train_mem_cpu_peaked_delta | 642937 |
  | train_mem_gpu_alloc_delta | 2267624448 |
  | train_mem_gpu_peaked_delta | 10355728896 |
  | train_runtime | 98.4931 | 
  | train_samples | 14732 |
  | train_samples_per_second | 3.533 |
  
  ## Eval results
  
  | key | value |
  | --- | ----- |
  | epoch | 3 |
  | eval_loss | 1.5356481075286865 |
  | eval_mem_cpu_alloc_delta | 659047 |
  | eval_mem_cpu_peaked_delta | 18254 |
  | eval_mem_gpu_alloc_delta | 0 |
  | eval_mem_gpu_peaked_delta | 300285440 |
  | eval_runtime | 0.3116 |
  | eval_samples | 818 |
  | eval_samples_per_second | 2625.337 |
  
  
  ## Usage
  ```python
  from transformers import pipeline
  summarizer = pipeline("summarization", model="philschmid/bart-base-samsum")
  
  conversation = '''Jeff: Can I train a 🤗 Transformers model on Amazon SageMaker? 
  Philipp: Sure you can use the 