readme: ---
  language: en
  tags:
  - summarization
  model-index:
  - name: google/pegasus-xsum
    results:
    - task:
        type: summarization
        name: Summarization
      dataset:
        name: samsum
        type: samsum
        config: samsum
        split: train
      metrics:
      - name: ROUGE-1
        type: rouge
        value: 21.8096
        verified: true
      - name: ROUGE-2
        type: rouge
        value: 4.2525
        verified: true
      - name: ROUGE-L
        type: rouge
        value: 17.4469
        verified: true
      - name: ROUGE-LSUM
        type: rouge
        value: 18.8907
        verified: true
      - name: loss
        type: loss
        value: 3.0317161083221436
        verified: true
      - name: gen_len
        type: gen_len
        value: 20.3122
        verified: true
    - task:
        type: summarization
        name: Summarization
      dataset:
        name: xsum
        type: xsum
        config: default
        split: test
      metrics:
      - name: ROUGE-1
        type: rouge
        value: 46.8623
        verified: true
      - name: ROUGE-2
        type: rouge
        value: 24.4533
        verified: true
      - name: ROUGE-L
        type: rouge
        value: 39.0548
        verified: true
      - name: ROUGE-LSUM
        type: rouge
        value: 39.0994
        verified: true
      - name: loss
        type: loss
        value: 1.5717021226882935
        verified: true
      - name: gen_len
        type: gen_len
        value: 22.8821
        verified: true
    - task:
        type: summarization
        name: Summarization
      dataset:
        name: cnn_dailymail
        type: cnn_dailymail
        config: 3.0.0
        split: test
      metrics:
      - name: ROUGE-1
        type: rouge
        value: 22.2062
        verified: true
      - name: ROUGE-2
        type: rouge
        value: 7.6701
        verified: true
      - name: ROUGE-L
        type: rouge
        value: 15.4046
        verified: true
      - name: ROUGE-LSUM
        type: rouge
        value: 19.2182
        verified: true
      - name: loss
        type: loss
        value: 2.681241273880005
        verified: true
      - name: gen_len
        type: gen_len
        value: 25.0234
        verified: true
  ---
  
  ### Pegasus Models
  See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
  
  Original TF 1 code [here](https://github.com/google-research/pegasus)
  
  Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
  
  Maintained by: [@sshleifer](https://twitter.com/sam_shleifer)
  
  Task: Summarization
  
  The following is copied from the authors' README.
  
  # Mixed & Stochastic Checkpoints
  
  We train a pegasus model with sampled gap sentence ratios on both C4 and HugeNews, and stochastically sample important sentences. The updated the results are reported in this table.
  
  | dataset | C4 | HugeNews | Mixed & Stochastic|
  | ---- | ---- | ---- | ----|
  | xsum | 45.20/22.06/36.99 | 47.21/24.56/39.25 | 47.60/24.83/39.64|
  | cnn_dailymail | 43.90/21.20/40.76 | 44.17/21.47/41.11 | 44.16/21.56/41.30|
  | newsroom | 45.07/33.39/41.28 | 45.15/33.51/41.33 | 45.98/34.20/42.18|
  | multi_news | 46.74/17.95/24.26 | 47.52/18.72/24.91 | 47.65/18.75/24.95|
  | gigaword | 38.75/19.96/36.14 | 39.12/19.86/36.24 | 39.65/20.47/36.76|
  | wikihow | 43.07/19.70/34.79 | 41.35/18.51/33.42 | 46.39/22.12/38.41 *|
  | reddit_tifu | 26.54/8.94/21.64 | 26.63/9.01/21.60 | 27.99/9.81/22.94|
  | big_patent | 53.63/33.16/42.25 | 53.41/32.89/42.07 | 52.29/33.08/41.66 *|
  | arxiv | 44.70/17.27/25.80 | 44.67/17.18/25.73 | 44.21/16.95/25.67|
  | pubmed | 45.49/19.90/27.69 | 45.09/19.56/27.42 | 45.97/20.15/28.25|
  | aeslc | 37.69/21.85/36.84 | 37.40/21.22/36.45 | 37.68/21.25/36.51|
  | billsum | 57.20/39.56/45.80 | 57.31/40.19/45.82 | 59.67/41.58/47.59|
  
  The "Mixed & Stochastic" model has the following changes:
  - trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples). 
  - trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity).
  - the model uniformly sample a gap sentence ratio between 15% and 45%.
  - importance sentences are sampled using a 20% uniform noise to importance scores.
  - the sentencepiece tokenizer is updated to be able to encode newline character.
  
  
  (*) the numbers of wikihow and big_patent datasets are not comparable because of change in tokenization and data:
  - wikihow dataset contains newline characters which is useful for paragraph segmentation, the C4 and HugeNews model's sentencepiece tokenizer doesn't encode newline and loose this information.
  - we update the BigPatent dataset to preserve casing, some format cleanings are also changed, please refer to change in TFDS.
  
  
  The "Mixed & Stochastic" model has the following changes (from pegasus-large in the paper):
  
  
  trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples).
  trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity).
  the model uniformly sample a gap sentence ratio between 15% and 45%.
  importance sentences are sampled using a 20% uniform noise to importance scores.
  the sentencepiece tokenizer 