Global AI chat room · 17 online now Join now
B
MODEL Listed

BART Large CNN

BART Large CNN is a sequence-to-sequence transformer model specifically fine-tuned on the CNN/Daily Mail dataset for abstractive summarization. Unlike general-purpose LLMs, it is optimized to condense long-form documents into concise, coherent summaries while maintaining factual consistency. With 406M parameters, it offers a lightweight footprint compared to modern frontier models, making it highly efficient for production environments where latency and cost are critical. Developers can easily integrate it via the Hugging Face Transformers library for automated news aggregation, document synthesis, or internal knowledge base pruning. It excels in tasks requiring structured summaries rather than open-ended creative generation.

Facebooksummarization
View model source
01 / MODEL CARD

Model card

BART Large CNN is a sequence-to-sequence transformer model specifically fine-tuned on the CNN/Daily Mail dataset for abstractive summarization. Unlike general-purpose LLMs, it is optimized to condense long-form documents into concise, coherent summaries while maintaining factual consistency. With 406M parameters, it offers a lightweight footprint compared to modern frontier models, making it highly efficient for production environments where latency and cost are critical. Developers can easily integrate it via the Hugging Face Transformers library for automated news aggregation, document synthesis, or internal knowledge base pruning. It excels in tasks requiring structured summaries rather than open-ended creative generation.

Model typesummarization
ProviderFacebook
LicenseApache 2.0
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/facebook/bart-large-cnn
View model source
Version informationUse the source repository for the latest version
—
03 / DOWNLOAD

Download this model

We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.

This entry points to Hugging Face. The commands use the matching ModelScope repository format; confirm that the repository exists on ModelScope before running them. Model repository: facebook/bart-large-cnn
Install ModelScope

Install the CLI and SDK dependency before downloading.

pip install modelscope
Download the full model repository

Download the complete weights, configuration and model card.

modelscope download --model facebook/bart-large-cnn
Download one file to a local directory

README.md is used as an example; replace it with another repository file when needed.

modelscope download --model facebook/bart-large-cnn README.md --local_dir ./dir
Download with the SDK

Useful in Python projects and automation scripts.

from modelscope import snapshot_download
model_dir = snapshot_download('facebook/bart-large-cnn')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/facebook/bart-large-cnn.git
Clone without downloading LFS blobs

Fetch the repository structure first, then pull large files when needed.

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/facebook/bart-large-cnn.git
04 / WORKFLOW

How to use

  1. 01
    Step 1

    Read the model card and source information.

  2. 02
    Step 2

    Start with a small, non-sensitive evaluation.

  3. 03
    Step 3

    Review quality, licensing and usage limits.

  4. 04
    Step 4

    Adopt it only after validation.

05 / DISCUSSIONS

Discussions

Use this space to keep checking source information, usage experience and maintenance status.

Open source page
Email