bart-large-cnn / README.md

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	---
	language:
	- en
	tags:
	- summarization
	license: mit
	thumbnail: https://huggingface.co/front/thumbnails/facebook.png
	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](https://huggingface.co/datasets/cnn_dailymail). It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) 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](https://huggingface.co/transformers/main_classes/pipelines.html):

	```python
	from transformers import pipeline

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

	ARTICLE = """ It’s generally prohibitive for IoT devices with restricted computation, memory, radio bandwidth, and battery resource to execute computational-intensive and latency-sensitive security
	tasks especially under heavy data streams [7]. However, most existing security solutions generate
	heavy computation and communication load for IoT devices, and outdoor IoT devices such as
	cheap sensors with light-weight security protections are usually more vulnerable to attacks than
	computer systems.
	"""
	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

	```bibtex
	@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}
	}