readme: ---
  language: en
  license: mit
  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:
        type: summarization
        name: Summarization
      dataset:
        name: 'SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization'
        type: samsum
      metrics:
      - type: rogue-1
        value: 42.621
        name: Validation ROGUE-1
      - type: rogue-2
        value: 21.9825
        name: Validation ROGUE-2
      - type: rogue-l
        value: 33.034
        name: Validation ROGUE-L
      - type: rogue-1
        value: 41.3174
        name: Test ROGUE-1
      - type: rogue-2
        value: 20.8716
        name: Test ROGUE-2
      - type: rogue-l
        value: 32.1337
        name: Test ROGUE-L
    - task:
        type: summarization
        name: Summarization
      dataset:
        name: samsum
        type: samsum
        config: samsum
        split: test
      metrics:
      - type: rouge
        value: 41.3282
        name: ROUGE-1
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTYzNzZkZDUzOWQzNGYxYTJhNGE4YWYyZjA0NzMyOWUzMDNhMmVhYzY1YTM0ZTJhYjliNGE4MDZhMjhhYjRkYSIsInZlcnNpb24iOjF9.OOM6l3v5rJCndmUIJV-2SDh2NjbPo5IgQOSL-Ju1Gwbi1voL5amsDEDOelaqlUBE3n55KkUsMLZhyn66yWxZBQ
      - type: rouge
        value: 20.8755
        name: ROUGE-2
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWZiODFiYWQzY2NmOTc5YjA3NTI0YzQ1MzQ0ODk2NjgyMmVlMjA5MjZiNTJkMGRmZGEzN2M3MDNkMjkxMDVhYSIsInZlcnNpb24iOjF9.b8cPk2-IL24La3Vd0hhtii4tRXujh5urAwy6IVeTWHwYfXaURyC2CcQOWtlOx5bdO5KACeaJFrFBCGgjk-VGCQ
      - type: rouge
        value: 32.1353
        name: ROUGE-L
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYWNmYzdiYWQ2ZWRkYzRiMGMxNWUwODgwZTdkY2NjZTc1NWE5NTFiMzU0OTU1N2JjN2ExYWQ2NGZkNjk5OTc4YSIsInZlcnNpb24iOjF9.Fzv4p-TEVicljiCqsBJHK1GsnE_AwGqamVmxTPI0WBNSIhZEhliRGmIL_z1pDq6WOzv3GN2YUGvhowU7GxnyAQ
      - type: rouge
        value: 38.401
        name: ROUGE-LSUM
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGI4MWY0NWMxMmQ0ODQ5MDhiNDczMDAzYzJkODBiMzgzYWNkMWM2YTZkZDJmNWJiOGQ3MmNjMGViN2UzYWI2ZSIsInZlcnNpb24iOjF9.7lw3h5k5lJ7tYFLZGUtLyDabFYd00l6ByhmvkW4fykocBy9Blyin4tdw4Xps4DW-pmrdMLgidHxBWz5MrSx1Bw
      - type: loss
        value: 1.4297215938568115
        name: loss
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzI0ZWNhNDM5YTViZDMyZGJjMDA1ZWFjYzNhOTdlOTFiNzhhMDBjNmM2MjA3ZmRkZjJjMjEyMGY3MzcwOTI2NyIsInZlcnNpb24iOjF9.oNaZsAtUDqGAqoZWJavlcW7PKx1AWsnkbhaQxadpOKk_u7ywJJabvTtzyx_DwEgZslgDETCf4MM-JKitZKjiDA
      - type: gen_len
        value: 60.0757
        name: gen_len
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTgwYWYwMDRkNTJkMDM5N2I2MWNmYzQ3OWM1NDJmODUyZGViMGE4ZTdkNmIwYWM2N2VjZDNmN2RiMDE4YTYyYiIsInZlcnNpb24iOjF9.PbXTcNYX_SW-BuRQEcqyc21M7uKrOMbffQSAK6k2GLzTVRrzZxsDC57ktKL68zRY8fSiRGsnknOwv-nAR6YBCQ
  ---
  
  ## `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"