The T5-large model, developed by Google Research, is designed to translate English text into other languages.
The Translation API endpoint allows you to translate English text into another language.
The translation is a Machine Learning task that uses artificial neural networks to translate text from one language to another. This API offers a multilingual model that translates the input text from a source language to a target language.
AI Endpoints makes it easy, with ready-to-use inference APIs. Discover how to use them:
This Translation API is based on an Open-Source model: google-t5/t5-large. It takes English text as input and returns its translation into an other language.
Model configuration
Warning: Note that you will have to use English source language and precise the target language in the model URL (e.g. French) as follows: https://t5-large.endpoints.kepler.ai.cloud.ovh.net/api/translate?from=English&target=French
The Translation endpoint offers you an optimized way to translate English text into an other language. Learn how to use them with the following example:
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 to get your translation:
import os
import requests
target_language = "French"
url = f"https://t5-large.endpoints.kepler.ai.cloud.ovh.net/api/translate?from=English&target={target_language}"
text = "Brian is in the kitchen"
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)
When using AI Endpoints, the following rate limits apply:
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.
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.
New to AI Endpoints? This guide walks you through everything you need to get an access token, call AI models, and integrate AI APIs into your apps with ease.
Start TutorialExplore what AI Endpoints can do. This guide breaks down current features, future roadmap items, and the platform's core capabilities so you know exactly what to expect.
Start TutorialRunning into issues? This guide helps you solve common problems on AI Endpoints, from error codes to unexpected responses. Get quick answers, clear fixes, and helpful tips to keep your projects running smoothly.
Start TutorialLearn how to use Structured Output with OVHcloud AI Endpoints.
Start TutorialLearn how to use Function Calling with OVHcloud AI Endpoints.
Start TutorialLearn how to use OVHcloud AI Endpoints Virtual Models.
Start TutorialCombine AI with your own data! This tutorial shows how to build a Python-based RAG chatbot that retrieves custom documents to enhance its answers.
Start TutorialCreate a Java-based RAG chatbot that blends LLMs with your own data. Learn how to use LangChain4j and AI Endpoints to build smart, document-aware assistants.
Start TutorialLearn how to use Structured Output with Java, LangChain4j and OVHcloud AI Endpoints.
Start TutorialLearn how to use Function Calling with Java, LangChain4j and OVHcloud AI Endpoints.
Start TutorialLearn how to use Model Context Protocol (MCP) with Java, LangChain4j and OVHcloud AI Endpoints.
Start Tutorial