Summarization [English]

docker pull cargoshipsh/summarization-en

Automatically summarize a text. This is a Bart-based model provided by Philipp Schmid on Huggingface and was trained on the SAMSum dataset that contains about 16k messenger-like conversations with summaries. The model itself is 1.6 GB in size and needs an aditional 1.5MB for the tokenizer. On a moderate CPU it takes only a few seconds to summarize a text.

Demo

License

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

System Requirements

Minimum: 2GB RAM, 1 vCPU
Recommended: 4GB RAM, 4 vCPU

Usage

Input [POST]

{
  "text": "When it comes to machine learning, everyone says it's going to
          make our lives easier - and it's true! But that's not true for
          most developers out there. ML has the potential to automate and
          revolutionise the way we work and live. However, developing machine
          learning models can be time consuming and resource intensive,
          requiring a significant amount of domain knowledge, data processing
          skills and technical expertise."
}

Output

{
  "text": "Developing machine learning models can be
          time consuming and resource intensive.
          It requires domain knowledge, data processing skills
          and technical expertise for most developers."
}

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.

Example

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

docker run -p 80:80 --env API_KEY=CHANGE_ME cargoshipsh/summarization-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":"When it comes to machine learning, everyone says it's going to make our lives easier - and it's true! But that's not true for most developers out there. ML has the potential to automate and revolutionise the way we work and live. However, developing machine learning models can be time consuming and resource intensive, requiring a significant amount of domain knowledge, data processing skills and technical expertise."}' http://localhost:80

You see the output of the model in the terminal.

{"text":"Developing machine learning models can be time consuming and resource intensive. It requires domain knowledge, data processing skills and technical expertise for most developers."}