distilbart cnn 12 6

Providersshleifer
Categorysummarization
Licenseapache-2.0
Downloads27
Stars0

Overview

DistilBART-cnn-12-6 is a streamlined version of the BART architecture, specifically optimized for abstractive summarization. By utilizing knowledge distillation, it retains the core linguistic capabilities of the larger BART model while significantly reducing latency and memory overhead. For developers, this means faster inference times and lower infrastructure costs without a drastic drop in summary quality. It is particularly effective for condensing news articles, documentation, or long-form reports into concise summaries. Integration is straightforward via the Hugging Face Transformers library, making it a plug-and-play choice for production pipelines where real-time processing is prioritized over the absolute nuance of a full-scale LLM.

Highlights

  • Optimized for fast, low-latency abstractive text summarization
  • Reduced memory footprint via efficient knowledge distillation
  • Seamless integration with Hugging Face Transformers library
  • Apache-2.0 license allows for flexible commercial deployment
  • Ideal for condensing news articles and long-form content

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("sshleifer/distilbart-cnn-12-6")
tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-cnn-12-6")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download sshleifer/distilbart-cnn-12-6

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download sshleifer/distilbart-cnn-12-6 config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('sshleifer/distilbart-cnn-12-6')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/sshleifer/distilbart-cnn-12-6

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/sshleifer/distilbart-cnn-12-6

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('sshleifer/distilbart-cnn-12-6')
tokenizer = AutoTokenizer.from_pretrained('sshleifer/distilbart-cnn-12-6')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance 
pip install modelscope

CLI Download

Download the full repository

Download the full repository 
modelscope download --model sshleifer/distilbart-cnn-12-6

Download a single file to a local folder (e.g. README.md into ./dir)

Download a single file to a local folder (e.g. README.md into ./dir) 
modelscope download --model sshleifer/distilbart-cnn-12-6 README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('sshleifer/distilbart-cnn-12-6')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://www.modelscope.cn/sshleifer/distilbart-cnn-12-6.git

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/sshleifer/distilbart-cnn-12-6.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

Install the ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

Load the model and run inference 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'sshleifer/distilbart-cnn-12-6')

Full Documentation

来源: HuggingFace

---
language: en
tags:

  • summarization

license: apache-2.0
datasets:
  • cnn_dailymail

  • xsum

thumbnail: https://huggingface.co/front/thumbnails/distilbart_medium.png
---

Usage

This checkpoint should be loaded into BartForConditionalGeneration.from_pretrained. See the BART docs for more information.

Metrics for DistilBART models

| Model Name | MM Params | Inference Time (MS) | Speedup | Rouge 2 | Rouge-L |
|:---------------------------|------------:|----------------------:|----------:|----------:|----------:|
| distilbart-xsum-12-1 | 222 | 90 | 2.54 | 18.31 | 33.37 |
| distilbart-xsum-6-6 | 230 | 132 | 1.73 | 20.92 | 35.73 |
| distilbart-xsum-12-3 | 255 | 106 | 2.16 | 21.37 | 36.39 |
| distilbart-xsum-9-6 | 268 | 136 | 1.68 | 21.72 | 36.61 |
| bart-large-xsum (baseline) | 406 | 229 | 1 | 21.85 | 36.50 |
| distilbart-xsum-12-6 | 306 | 137 | 1.68 | 22.12 | 36.99 |
| bart-large-cnn (baseline) | 406 | 381 | 1 | 21.06 | 30.63 |
| distilbart-12-3-cnn | 255 | 214 | 1.78 | 20.57 | 30.00 |
| distilbart-12-6-cnn | 306 | 307 | 1.24 | 21.26 | 30.59 |
| distilbart-6-6-cnn | 230 | 182 | 2.09 | 20.17 | 29.70 |

View official source
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