Roberta-base-go_emotions

Emotions Analysis API

The Emotions Analysis API endpoint allows you to extract the different emotions from text.

Introduction

Emotions Analysis task refers to the process of automatically identifying and extracting emotional information from text. Emotion extraction typically involves analyzing language patterns, sentence structure, and other linguistic features to infer the emotional state of the person who wrote.

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

Model concept and configuration

This Emotions Analysis API is based on an Open-Source model: SamLowe/roberta-base-go_emotions. It takes text as input and returns the text's emotions and the corresponding probability.

Model configuration:

• Input type: Raw text
• Language Support: English
• Output classes: admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, surprise, neutral

How to?

The Emotion Analysis endpoint offers you an optimized way to extract the different emotions 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://roberta-base-go-emotions.endpoints.kepler.ai.cloud.ovh.net/api/text2emotions"
text = "I'm very happy and this sentence should be converted into a 'joy' emotion."
 
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.