readme: 
  ---
  language: en
  tags:
  - sagemaker
  - bart
  - summarization
  license: apache-2.0
  datasets:
  - samsum
  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: 40.8911
        verified: true
      - name: ROUGE-2
        type: rouge
        value: 20.3551
        verified: true
      - name: ROUGE-L
        type: rouge
        value: 31.2696
        verified: true
      - name: ROUGE-LSUM
        type: rouge
        value: 37.9313
        verified: true
      - name: loss
        type: loss
        value: 1.4995627403259277
        verified: true
      - name: gen_len
        type: gen_len
        value: 60.2247
        verified: true
  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 "
  ---
  ## `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](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
      {
      "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,
      "sagemaker_container_log_level": 20,
      "sagemaker_job_name": "huggingface-pytorch-training-2021-09-08-06-40-19-182",
      "sagemaker_program": "run_summarization.py",
      "sagemaker_region": "us-west-2",
      "sagemaker_submit_directory": "s3://sagemaker-us-west-2-847380964353/huggingface-pytorch-training-2021-09-08-06-40-19-182/source/sourcedir.tar.gz",
      "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 | 42.059 |
  | eval_rouge2 | 21.5509 |
  | eval_rougeL | 32.4083 |
  | eval_rougeLsum | 39.0015 |
  | test_rouge1 | 40.8656 |
  | test_rouge2 | 20.3517 |
  | test_rougeL | 31.2268 |
  | test_rougeLsum | 37.9301 |
  