https://xenova.github.io/transformers.js/

Transformers.js

  * Demo
  * Getting Started
  * Usage
  * Examples

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Transformers.js

Run  Transformers in your browser!

Get Started View Source

Demo

Don't believe us? Play around with some of these models:

Task:
[Translation w/ t5-small (95 MB)                                         ]
Languages:
[English]
to
[French  ]
Parameters:
Max length [50                  ]
No. beams [1                   ]
No. samples [5                   ]
Temp. [1                   ]
Top K [20                  ]
Sample [Yes]
[The Shawshank Redemp]
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[The goal of life is ]
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Context:

[The Amazon rainfores]
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Question:

[What proportion of t]
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Answer:

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[Hello, how are you? ]
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[The tower is 324 met]
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[I enjoy walking with]
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[def fib(n):         ]

                                    def fib(n):


Audio:
[Example 1] [                    ] Your browser does not support the
audio element.
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Image: [Example 1] [                    ] [football-m]
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Image: [Example 1] [                    ] [tiger]
Image: [Example 1] [                    ] [football-m]
Possible class names (comma-separated): [football, airport, a]
Notes:

  * Clicking Generate for the first time will download the
    corresponding model from the HuggingFace Hub. All subsequent
    requests will use the cached model.
  * For more information about the different parameters, check out
    HuggingFace's guide to text generation.

Generate
Loading model files... (only run once)

Getting Started

Installation

If you use npm, you can install it using:

npm i @xenova/transformers

Alternatively, you can use it in a <script> tag from a CDN, for
example:

<!-- Using jsDelivr -->
<script src="https://cdn.jsdelivr.net/npm/@xenova/transformers/dist/transformers.min.js"></script>

<!-- or UNPKG -->
<script src="https://www.unpkg.com/@xenova/transformers/dist/transformers.min.js"></script>

Basic example

It's super easy to translate from existing code!

from transformers import pipeline

# Allocate a pipeline for sentiment-analysis
pipe = pipeline('sentiment-analysis')

out = pipe('I love transformers!')
# [{'label': 'POSITIVE', 'score': 0.999806941}]

Python (original)

import { pipeline } from "@xenova/transformers";

// Allocate a pipeline for sentiment-analysis
let pipe = await pipeline('sentiment-analysis');

let out = await pipe('I love transformers!');
// [{'label': 'POSITIVE', 'score': 0.999817686}]

JavaScript (ours)


In the same way as the Python library, you can use a different model
by providing its name as the second argument to the pipeline
function. For example:

// Use a different model for sentiment-analysis
let pipe = await pipeline('sentiment-analysis', 'nlptown/bert-base-multilingual-uncased-sentiment');

Custom setup

By default, Transformers.js uses hosted models precompiled WASM
binaries, which should work out-of-the-box. You can override this
behaviour as follows:

import { env } from "@xenova/transformers";

// Use a different host for models.
// - `remoteURL` defaults to use the HuggingFace Hub
// - `localURL` defaults to '/models/onnx/quantized/'
env.remoteURL = 'https://www.example.com/';
env.localURL = '/path/to/models/';

// Set whether to use remote or local models. Defaults to true.
//  - If true, use the path specified by `env.remoteURL`.
//  - If false, use the path specified by `env.localURL`.
env.remoteModels = false;

// Set parent path of .wasm files. Defaults to use a CDN.
env.onnx.wasm.wasmPaths = '/path/to/files/';

Usage

We currently support the following tasks and models, which can be
used with the pipeline function.

 1. sentiment-analysis (a.k.a. text-classification)
    Supported models:
    distilbert-base-uncased-finetuned-sst-2-english, nlptown/
    bert-base-multilingual-uncased-sentiment, distilgpt2. For more
    information, check out the Text Classification docs .
 2. question-answering
    Supported models: distilbert-base-cased-distilled-squad,
    distilbert-base-uncased-distilled-squad. For more information,
    check out the Question Answering docs .
 3. fill-mask
    Supported models: xlm-roberta-base, albert-large-v2,
    albert-base-v2, distilroberta-base, roberta-base,
    bert-base-cased, bert-base-uncased,
    bert-base-multilingual-uncased, bert-base-multilingual-cased,
    distilbert-base-cased, distilbert-base-uncased. For more
    information, check out the Language Modelling docs .
 4. summarization
    Supported models: t5-small, t5-base, t5-v1_1-small, t5-v1_1-base,
    sshleifer/distilbart-cnn-6-6, sshleifer/distilbart-cnn-12-6. For
    more information, check out the Summarization docs .
 5. translation (translation_en_to_de, translation_en_to_fr,
    translation_en_to_ro)
    Supported models: t5-small, t5-base, t5-v1_1-small, t5-v1_1-base.
    For more information, check out the Translation docs .
 6. text2text-generation
    Supported models: google/flan-t5-small, google/flan-t5-base,
    t5-small, t5-base, google/t5-v1_1-small, google/t5-v1_1-base,
    sshleifer/distilbart-cnn-6-6, sshleifer/distilbart-cnn-12-6. For
    more information, check out the Text Generation docs .
 7. text-generation
    Supported models: gpt2, distilgpt2, Salesforce/codegen-350M-mono,
    Salesforce/codegen-350M-multi, Salesforce/codegen-350M-nl. For
    more information, check out the Text Generation docs .
 8. automatic-speech-recognition
    Supported models: openai/whisper-tiny.en, openai/whisper-tiny,
    openai/whisper-small.en, openai/whisper-small, openai/
    whisper-base.en, openai/whisper-base. For more information, check
    out the Automatic Speech Recognition docs .
 9. image-to-text
    Supported models: nlpconnect/vit-gpt2-image-captioning. For more
    information, check out the Image-to-Text docs .
10. image-classification
    Supported models: google/vit-base-patch16-224. For more
    information, check out the Image Classification docs .
11. zero-shot-image-classification
    Supported models: openai/clip-vit-base-patch16, openai/
    clip-vit-base-patch32. For more information, check out the
    Zero-Shot Image Classification .
12. embeddings
    Supported models: sentence-transformers/all-MiniLM-L6-v2,
    sentence-transformers/all-MiniLM-L12-v2, sentence-transformers/
    all-distilroberta-v1, sentence-transformers/
    paraphrase-albert-base-v2, sentence-transformers/
    paraphrase-albert-small-v2. For more information, check out the
    Embeddings docs .


The following model types are supported:

 1. BERT
    Tasks: Masked language modelling (AutoModelForMaskedLM), question
    answering (AutoModelForQuestionAnswering), and sequence
    classification (AutoModelForSequenceClassification). For more
    information, check out the BERT docs.
 2. ALBERT
    Tasks: Masked language modelling (AutoModelForMaskedLM). For more
    information, check out the ALBERT docs.
 3. DistilBERT
    Tasks: Masked language modelling (AutoModelForMaskedLM), question
    answering (AutoModelForQuestionAnswering), and sequence
    classification (AutoModelForSequenceClassification). For more
    information, check out the DistilBERT docs.
 4. T5
    Tasks: Sequence-to-sequence for translation/summarization
    (AutoModelForSeq2SeqLM). For more information, check out the T5
    docs.
 5. T5v1.1
    Tasks: Sequence-to-sequence (AutoModelForSeq2SeqLM). For more
    information, check out the T5v1.1 docs.
 6. FLAN-T5
    Tasks: Sequence-to-sequence for over 1000 tasks
    (AutoModelForSeq2SeqLM). For more information, check out the
    FLAN-T5 docs.
 7. GPT2/DistilGPT2
    Tasks: Text generation (AutoModelForCausalLM). For more
    information, check out the GPT2 docs or DistilGPT2 docs.
 8. BART
    Tasks: Sequence-to-sequence for summarization
    (AutoModelForSeq2SeqLM). For more information, check out the BART
    docs.
 9. CodeGen
    Tasks: Text generation (AutoModelForCausalLM). For more
    information, check out the CodeGen docs.
10. Whisper
    Tasks: Automatic speech recognition (AutoModelForSeq2SeqLM). For
    more information, check out the Whisper docs.
11. CLIP
    Tasks: Zero-shot Image classification (AutoModel). For more
    information, check out the CLIP docs.
12. Vision Transformer (ViT)
    Tasks: Image classification (AutoModelForImageClassification).
    For more information, check out the Vision Transformer docs.
13. VisionEncoderDecoderModel
    Tasks: Image to text (AutoModelForVision2Seq). For more
    information, check out the Vision Encoder Decoder Models docs.


Don't see your model type or task supported? Raise an issue on
GitHub, and if there's enough demand, we will add it!

We use ONNX Runtime to run the models in the browser, so you must
first convert your PyTorch model to ONNX (which can be done using our
conversion script).

Examples

Coming soon... In the meantime, check out the source code for the
demo here.

Copyright (c) Xenova 2023