Machine Learning¶

const {PipelineOptions, EngineProcess } = ChromeUtils.importESModule("chrome://global/content/ml/EngineProcess.sys.mjs");
const options = new PipelineOptions(
  {
  taskName: "summarization",
  modelId: "mozilla/text_summarization",
  modelRevision: "main"
  }
);

const engineParent = await EngineProcess.getMLEngineParent();
const engine = engineParent.getEngine(options);

const text = 'The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, ' +
'and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. ' +
'During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest ' +
'man-made structure in the world, a title it held for 41 years until the Chrysler Building in New ' +
'York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to ' +
'the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the ' +
'Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second ' +
'tallest free-standing structure in France after the Millau Viaduct.';

const request = { data:  text, options: { max_length: 100 } };
const res = await engine.run(request);
console.log(res[0]["summary_text"]);

const {PipelineOptions, EngineProcess } = ChromeUtils.importESModule("chrome://global/content/ml/EngineProcess.sys.mjs");

// First we create a pipeline options object, which contains the task name
// and any other options needed for the task
const options = new PipelineOptions({taskName: "moz-image-to-text" });

// Next, we create an engine parent object via EngineProcess
const engineParent = await EngineProcess.getMLEngineParent();

// We then create the engine object, using the options
const engine = engineParent.getEngine(options);

// Preparing a request
const request = {url: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg"};

// At this point we are ready to do some inference.
const res = await engine.run(request);

// The result is a string containing the text extracted from the image
console.log(res);