Bert-base-multilingual-uncased-sentiment

Sentiment Analysis API

The Sentiment Analysis API endpoint allows you to analyze your text sentiment.

Introduction

Sentiment Analysis on text involves analyzing the words, phrases, and sentence structures used in the text data to determine the underlying sentiment, which can be positive, negative, neutral, etc. The Sentiment Analysis model can classify the text data based on predefined sentiment categories.

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

Model concept and configuration

This Sentiment Analysis API is based on an Open-Source model: nlptown/bert-base-multilingual-uncased-sentiment. It takes text as input and returns the text's feelings and the corresponding probability.

Model configuration:

• Input type: Raw text
• Language Support: English, Dutch, German, French, Italian, Spanish
• Output classes: 5 stars, 4 stars, 3 stars, 2 stars, 1 star

Warning:

Note that this model predicts the sentiment of the input text as a number of stars:

• 1 ⭐ is very negative
• 5 ⭐⭐⭐⭐⭐ is very positive

How to?

The Sentiment Analysis endpoint offers you an optimized way to analyze the feeling of a 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 = "https://bert-base-multilingual-uncased-sentiment.endpoints.kepler.ai.cloud.ovh.net/api/text2sentiments"
text = "This AI Endpoint is really useful for classifying a text's feelings from 1 to 5 stars."
 
headers = {
    "Content-Type": "application/json",
    "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.json()
    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.