readme: ---
  language: en
  tags:
  - bart
  - seq2seq
  - summarization
  license: apache-2.0
  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-base-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 ROUGE-1
        type: rouge-1
        value: 46.6619
      - name: Validation ROUGE-2
        type: rouge-2
        value: 23.3285
      - name: Validation ROUGE-L
        type: rouge-l
        value: 39.4811
      - name: Test ROUGE-1
        type: rouge-1
        value: 44.9932
      - name: Test ROUGE-2
        type: rouge-2
        value: 21.7286
      - name: Test ROUGE-L
        type: rouge-l
        value: 38.1921
    - task:
        type: summarization
        name: Summarization
      dataset:
        name: samsum
        type: samsum
        config: samsum
        split: test
      metrics:
      - name: ROUGE-1
        type: rouge
        value: 45.0148
        verified: true
      - name: ROUGE-2
        type: rouge
        value: 21.6861
        verified: true
      - name: ROUGE-L
        type: rouge
        value: 38.1728
        verified: true
      - name: ROUGE-LSUM
        type: rouge
        value: 41.2794
        verified: true
      - name: loss
        type: loss
        value: 1.597476601600647
        verified: true
      - name: gen_len
        type: gen_len
        value: 17.6606
        verified: true
  ---
  ## `bart-base-samsum`
  This model was obtained by fine-tuning `facebook/bart-base` on Samsum dataset.
  
  ## Usage
  ```python
  from transformers import pipeline
  
  summarizer = pipeline("summarization", model="lidiya/bart-base-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)
  ```
  
  ## Training procedure
  - Colab notebook: https://colab.research.google.com/drive/1RInRjLLso9E2HG_xjA6j8JO3zXzSCBRF?usp=sharing
  
  ## Results
  | key | value |
  | --- | ----- |
  | eval_rouge1 | 46.6619 |
  | eval_rouge2 | 23.3285 |
  | eval_rougeL | 39.4811 |
  | eval_rougeLsum | 43.0482 |
  | test_rouge1 | 44.9932 |
  | test_rouge2 | 21.7286 |
  | test_rougeL | 38.1921 |
  | test_rougeLsum | 41.2672 |
  