Optimizations

[xenova]

• Refactoring PretrainedModel
• New model support:

  • Camembert (models):

    let pipe = await pipeline('token-classification', 'Xenova/camembert-ner-with-dates');
    let output = await pipe("Je m'appelle jean-baptiste et j'habite à montréal depuis fevr 2012");
    // [
    //   { entity: 'I-PER', score: 0.9258053302764893, index: 5, word: 'jean' },
    //   { entity: 'I-PER', score: 0.9048717617988586, index: 6, word: '-' },
    //   { entity: 'I-PER', score: 0.9227054119110107, index: 7, word: 'ba' },
    //   { entity: 'I-PER', score: 0.9385354518890381, index: 8, word: 'pt' },
    //   { entity: 'I-PER', score: 0.9139659404754639, index: 9, word: 'iste' },
    //   { entity: 'I-LOC', score: 0.9877734780311584, index: 15, word: 'montré' },
    //   { entity: 'I-LOC', score: 0.9891639351844788, index: 16, word: 'al' },
    //   { entity: 'I-DATE', score: 0.9858269691467285, index: 18, word: 'fe' },
    //   { entity: 'I-DATE', score: 0.9780661463737488, index: 19, word: 'vr' },
    //   { entity: 'I-DATE', score: 0.980688214302063, index: 20, word: '2012' }
    // ]

    

  • GPT-J (models):

    import { pipeline } from '@xenova/transformers';
    
    const generator = await pipeline('text-generation', 'Xenova/J-350M');
    const prompt = 'def fib(n):'
    const output = await generator(prompt, {
      max_new_tokens: 50,
    })
    // {
    //   generated_text: 'def fib(n):\n' +
    //     '    if n == 0:\n' +
    //     '        return 1\n' +
    //     '    elif n == 1:\n' +
    //     '        return fib(n - 1)\n' +
    //     '    else:\n' +
    //     '        return fib(n - 1) * fib(n - 2)\n' +
    //     '\n'
    // }

  • WavLM (Closes [Question] WavLM support  #75). (models)

    import { AutoProcessor, AutoModel, read_audio } from '@xenova/transformers';
    
    // Read and preprocess audio
    const processor = await AutoProcessor.from_pretrained('Xenova/wavlm-base');
    const audio = await read_audio('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav', 16000);
    const inputs = await processor(audio);
    
    // Run model with inputs
    const model = await AutoModel.from_pretrained('Xenova/wavlm-base');
    const output = await model(inputs);
    // {
    //   last_hidden_state: Tensor {
    //     dims: [ 1, 549, 768 ],
    //     type: 'float32',
    //     data: Float32Array(421632) [-0.349443256855011, -0.39341306686401367,  0.022836603224277496, ...],
    //     size: 421632
    //   }
    // }

  • MBart (+MBart50). See here for the full list of languages and their corresponding codes. (models)

    let translator = await pipeline('translation', 'Xenova/mbart-large-50-many-to-many-mmt');
    let output = await translator('संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है', {
      src_lang: 'hi_IN', // Hindi
      tgt_lang: 'fr_XX', // French
    });
    // [{ translation_text: 'Le chef des Nations affirme qu 'il n 'y a military solution in Syria.' }]

  • OPT (models):

    const generator = await pipeline('text-generation', 'Xenova/opt-125m', {
        quantized: false, // NOTE: quantized models are currently broken
    });
    const prompt = 'Once upon a';
    const output = await generator(prompt);
    // [{ generated_text: 'Once upon a time, I was a student at the University of California, Berkeley. I was' }]

  • BeiT (models):

    let url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
    let pipe = await pipeline('image-classification', 'Xenova/beit-base-patch16-224');
    let output = await pipe(url);
    // [{ label: 'tiger, Panthera tigris', score: 0.7168469429016113 }]

  • ResNet (models):

    let url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
    let pipe = await pipeline('image-classification', 'Xenova/resnet-50');
    let output = await pipe(url);
    // [{ label: 'tiger, Panthera tigris', score: 0.7576608061790466 }]

[HuggingFaceDocBuilderDev]

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