Sentiment Analysis [English]

docker pull cargoshipsh/sentiment-en

Automatically detect the language of an input text. This is a roBERTa-base model was provided by Cardiff NLP and trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark. The model itself is 500MB and needs an aditional 1.5MB for the tokenizer.

Demo

Input

Predicted Sentiment

positive

License

The code for the API wrapper is licensed under MIT License.

System Requirements

Minimum: 1GB RAM, 1 vCPU
Recommended: 2GB RAM, 2 vCPU

API

If you don't want to implement the model all by yourself, no worries. Benefit from our easy to use API and get started right away!

Get Started

Usage

Input [POST]

{
  "text": "I like you."
}

Output

{
  "sentiment": "positive"
}

You need to set an API Key via the environment variable API_KEY to run the image and set the X-API-KEY header in your request with the same KEY.

Need a more detailed setup guide?

To get more detailed instructions how to get started please check out our quick start guide in the docs.

Example

Make sure you have Docker installed then run the following command:

docker run -p 80:80 --env API_KEY=CHANGE_ME cargoshipsh/sentiment-en

In a new terminal window, run the following command to call the API

curl -X POST -H 'Content-type: application/json' -H 'X-API-Key: CHANGE_ME' --data '{"text":"I like you!"}' http://localhost:80

You see the output of the model in the terminal.

{"sentiment":"positive"}

Need help?

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