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facebook_bart-large-cnn

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Model card

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language:

  • en pipeline_tag: summarization license: mit thumbnail: https://huggingface.co/front/thumbnails/facebook.png datasets:
  • cnn_dailymail model-index:
  • name: facebook/bart-large-cnn results:
    • task: type: summarization name: Summarization dataset: name: cnn_dailymail type: cnn_dailymail config: 3.0.0 split: train metrics:
      • name: ROUGE-1 type: rouge value: 42.9486 verified: true
      • name: ROUGE-2 type: rouge value: 20.8149 verified: true
      • name: ROUGE-L type: rouge value: 30.6186 verified: true
      • name: ROUGE-LSUM type: rouge value: 40.0376 verified: true
      • name: loss type: loss value: 2.529000997543335 verified: true
      • name: gen_len type: gen_len value: 78.5866 verified: true

BART (large-sized model), fine-tuned on CNN Daily Mail

BART model pre-trained on English language, and fine-tuned on CNN Daily Mail. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in [this repository (https://github.com/pytorch/fairseq/tree/master/examples/bart).

Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.

Model description

BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.

BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). This particular checkpoint has been fine-tuned on CNN Daily Mail, a large collection of text-summary pairs.

Intended uses & limitations

You can use this model for text summarization.

How to use

Here is how to use this model with the pipeline API:

from transformers import pipeline

summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

ARTICLE = """ New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York.
A year later, she got married again in Westchester County, but to a different man and without divorcing her first husband.
Only 18 days after that marriage, she got hitched yet again. Then, Barrientos declared "I do" five more times, sometimes only within two weeks of each other.
In 2010, she married once more, this time in the Bronx. In an application for a marriage license, she stated it was her "first and only" marriage.
Barrientos, now 39, is facing two criminal counts of "offering a false instrument for filing in the first degree," referring to her false statements on the
2010 marriage license application, according to court documents.
Prosecutors said the marriages were part of an immigration scam.
On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to her attorney, Christopher Wright, who declined to comment further.
After leaving court, Barrientos was arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New York subway through an emergency exit, said Detective
Annette Markowski, a police spokeswoman. In total, Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002.
All occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be married to four men, and at one time, she was married to eight men at once, prosecutors say.
Prosecutors said the immigration scam involved some of her husbands, who filed for permanent residence status shortly after the marriages.
Any divorces happened only after such filings were approved. It was unclear whether any of the men will be prosecuted.
The case was referred to the Bronx District Attorney\'s Office by Immigration and Customs Enforcement and the Department of Homeland Security\'s
Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt, Turkey, Georgia, Pakistan and Mali.
Her eighth husband, Rashid Rajput, was deported in 2006 to his native Pakistan after an investigation by the Joint Terrorism Task Force.
If convicted, Barrientos faces up to four years in prison.  Her next court appearance is scheduled for May 18.
"""
print(summarizer(ARTICLE, max_length=130, min_length=30, do_sample=False))
>>> [{'summary_text': 'Liana Barrientos, 39, is charged with two counts of "offering a false instrument for filing in the first degree" In total, she has been married 10 times, with nine of her marriages occurring between 1999 and 2002. She is believed to still be married to four men.'}]

BibTeX entry and citation info

@article{DBLP:journals/corr/abs-1910-13461,
  author    = {Mike Lewis and
               Yinhan Liu and
               Naman Goyal and
               Marjan Ghazvininejad and
               Abdelrahman Mohamed and
               Omer Levy and
               Veselin Stoyanov and
               Luke Zettlemoyer},
  title     = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language
               Generation, Translation, and Comprehension},
  journal   = {CoRR},
  volume    = {abs/1910.13461},
  year      = {2019},
  url       = {http://arxiv.org/abs/1910.13461},
  eprinttype = {arXiv},
  eprint    = {1910.13461},
  timestamp = {Thu, 31 Oct 2019 14:02:26 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

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Files & hashes

PathSizesha1sha256
README.md5.9 KB (6,009 B)1d584a0b508dbbf02b07d5139862134acd66d1d1a49b4b7fa5e64a05277d57cb767d9b8e8d4789a09cab9647beadceaaed86e170
config.json1.5 KB (1,585 B)2d297163111d6483909070dc6de8d34236c72636c6cb642aec929b65f514ee0ec7c04f9de19f705c143491577ecd8b7cc923c6ed
generation_config.json363 B (363 B)f729590ec05c715e3b922996b3a7b5c1c73fb1f74897361917410254e3132e8fe7786d37f3ef7cff54a650845c1147c2450a790f
generation_config_for_summarization.json363 B (363 B)f729590ec05c715e3b922996b3a7b5c1c73fb1f74897361917410254e3132e8fe7786d37f3ef7cff54a650845c1147c2450a790f
merges.txt445.6 KB (456,318 B)226b0752cac7789c48f0cb3ec53eda48b7be36cc1ce1664773c50f3e0cc8842619a93edc4624525b728b188a9e0be33b7726adc5
model.safetensors1.51 GB (1,625,222,120 B)2701c15a4c8db6ec14d966add20e7a1fed92619840041830399afb5348525ef8354b007ecec4286fdf3524f7e6b54377e17096cb
pytorch_model.bin1.51 GB (1,625,270,765 B)6a4214592533177630b530bf2b64bc2531b3918d2ac2745c02ac987d82c78a14b426de58d5e4178ae8039ba1c6881eccff3e82f1
rust_model.ot1.90 GB (2,037,049,936 B)ae9a208a098a3753e2bbda388c71fdc361a15abccd0d1586babffa4e90ca71e230290b55b8ebf634319a1c4200c8506ddbae0ab0
tokenizer.json1.3 MB (1,355,863 B)ad0bcbeb288f0d1373d88e0762e66357f55b8311847bbeab6174d66a88898f729d52fa8d355fafe1bea101cf960dd404581df70e
vocab.json877.8 KB (898,823 B)5606f48548d99a9829d10a96cd364b816b02cd219e7f63c2d15d666b52e21d250d2e513b87c9b713cfa6987a82ed89e5e6e50655

Cite this release

Canonical URL
https://aiseedbank.org/models/facebook_bart-large-cnn/
Slug
facebook_bart-large-cnn
Infohash
03554b862e24b57104cc3a701d5236c4f74f5948
License
mit
Signing key fingerprint
85a3b32c3712427b

Every file carries a locally computed sha256 — verify a download against the signed sums: facebook_bart-large-cnn.SHA256SUMS (+ minisign signature).

Provenance

Upstream repositoryfacebook/bart-large-cnn
Revision (pinned)37f520fa929c961707657b28798b30c003dd100b
Fetched at2026-09-03T22:28:09Z
License at fetchmit
Snapshot toolhuggingface · seedbank 0.1.0

Trackers

✓ verified · rehash-vs-hf-metadata at 2026-09-03T22:29:14Z

mit4.93 GB (5,290,262,145 bytes)transformerspytorchjaxrustsafetensorsbarttext2text-generationsummarizationmodel-indexendpoints_compatible2 languages (tf, en)paper: 1910.13461