Bart Large Tos

🚀 BART (Large-sized Model), Fine-tuned on Custom Contracts Dataset

This is a BART model pre-trained on the English language and fine-tuned for terms of service abstractive summarization.

🚀 Quick Start

This BART model, pre - trained on English, is fine - tuned for text summarization, especially for terms of service. You can use it easily with the pipeline API.

✨ Features

• Powerful Architecture: BART is a transformer encoder - encoder (seq2seq) model. It has a bidirectional (BERT - like) encoder and an autoregressive (GPT - like) decoder.
• Versatile Applications: Effective for text generation tasks such as summarization and translation, and also performs well in comprehension tasks like text classification and question answering.
• Fine - tuned on Specific Dataset: This particular checkpoint has been fine - tuned on CNN Daily Mail, a large collection of text - summary pairs.

📦 Installation

Not provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

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

from transformers import pipeline

summarizer = pipeline("summarization", model="ML-unipi/bart-large-tos")

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.'}]

📚 Documentation

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.

🔧 Technical Details

Not provided in the original document, so this section is skipped.

📄 License

This model is licensed under the MIT license.