Bart-large-cnn

Summarization API

The Summarization API endpoint allows you to summarize the input text in a few words.

Introduction

Summarization is a Text Generation task. This technology refers to the process of generating shortened versions of long texts by extracting the main points, ideas, and important information. This involves using techniques from Machine Learning to understand the original content and distill it down into a condensed format. 

AI Endpoints makes it easy, with ready-to-use inference APIs. Discover how to use them:

Model concept and configuration

This Summarization API is based on an Open-Source model: facebook/bart-large-cnn. It takes text as input and returns his summary in a few lines.

Model configuration:

• Input type: Raw text
• Language Support: English

How to?

The Summarization endpoint offers you an optimized way to generate a shortened version of your text. Learn how to use them with the following example:

With a simple HTTP client (requests)

First install the requests library:

pip install requests

Next, export your access token to the OVH_AI_ENDPOINTS_ACCESS_TOKEN environment variable:

export OVH_AI_ENDPOINTS_ACCESS_TOKEN=<your-access-token>

If you do not have an access token key yet, follow the instructions in the AI Endpoints – Getting Started.

Finally, run the following Python code:

import os
import requests

url = f"https://bart-large-cnn.endpoints.kepler.ai.cloud.ovh.net/api/summarize"
text = 'Deep learning is the subset of machine learning methods based on artificial neural networks (ANNs) with representation learning. \
    The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised, \
    semi-supervised or unsupervised.Deep-learning architectures such as deep neural networks, deep belief networks, \
    recurrent neural networks, convolutional neural networks and transformers have been applied to fields including computer vision, \
    speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, \
    climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. \
    Artificial neural networks were inspired by information processing and distributed communication nodes in biological systems. \
    ANNs have various differences from biological brains. Specifically, artificial neural networks tend to be static and symbolic, \
    while the biological brain of most living organisms is dynamic (plastic) and analog. ANNs are generally seen as low quality models for brain function.' 
 
headers = {
    "Content-Type": "text/plain",
    "Authorization": f"Bearer {os.getenv('OVH_AI_ENDPOINTS_ACCESS_TOKEN')}",
}

response = requests.post(url, data=text, headers=headers)
if response.status_code == 200:
    # Handle response
    response_data = response.text
    print(response_data)
else:
    print("Error:", response.status_code, response.text)

Model rate limit

When using AI Endpoints, the following rate limits apply:

• Anonymous: 2 requests per minute, per IP and per model.
• Authenticated with an API access key: 400 requests per minute, per Public Cloud project and per model.

If you exceed this limit, a 429 error code will be returned.

If you require higher usage, please get in touch with us to discuss increasing your rate limits.

Going Further

For a broader overview of AI Endpoints, explore the full AI Endpoints Documentation.

Reach out to our support team or join the OVHcloud Discord #ai-endpoints channel to share your questions, feedback, and suggestions for improving the service, to the team and the community.