Back to all models
summarization mask_token:
Query this model
🔥 This model is currently loaded and running on the Inference API. ⚠️ This model could not be loaded by the inference API. ⚠️ This model can be loaded on the Inference API on-demand.
JSON Output
API endpoint  

⚡️ Upgrade your account to access the Inference API

							$
							curl -X POST \
-H "Authorization: Bearer YOUR_ORG_OR_USER_API_TOKEN" \
-H "Content-Type: application/json" \
-d '"json encoded string"' \
https://api-inference.huggingface.co/models/google/pegasus-gigaword
Share Copied link to clipboard

Monthly model downloads

google/pegasus-gigaword google/pegasus-gigaword
4,772 downloads
last 30 days

pytorch

tf

Contributed by

Google AI company
3 team members · 54 models

How to use this model directly from the 🤗/transformers library:

			
Copy to clipboard
from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("google/pegasus-gigaword") model = AutoModelWithLMHead.from_pretrained("google/pegasus-gigaword")
Uploaded in S3

Pegasus Models

See Docs: here

Original TF 1 code here

Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019

Maintained by: @sshleifer

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 is updated to be able to encode newline character.

Citation



@misc{zhang2019pegasus,
    title={PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization},
    author={Jingqing Zhang and Yao Zhao and Mohammad Saleh and Peter J. Liu},
    year={2019},
    eprint={1912.08777},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}