Transformers.js

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The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain "Amazonas" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species.

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                                    def fib(n):

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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.

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.

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.
question-answering
Supported models: distilbert-base-cased-distilled-squad, distilbert-base-uncased-distilled-squad. For more information, check out the Question Answering docs.
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.
summarization
Supported models: t5-small, t5-base, t5-v1_1-small, t5-v1_1-base, facebook/bart-large-cnn, sshleifer/distilbart-cnn-6-6, sshleifer/distilbart-cnn-12-6. For more information, check out the Summarization docs.
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.
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, google/mt5-small, facebook/bart-large-cnn, sshleifer/distilbart-cnn-6-6, sshleifer/distilbart-cnn-12-6. For more information, check out the Text Generation docs.
text-generation
Supported models: gpt2, distilgpt2, EleutherAI/gpt-neo-125M, Salesforce/codegen-350M-mono, Salesforce/codegen-350M-multi, Salesforce/codegen-350M-nl. For more information, check out the Text Generation docs.
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.
image-to-text
Supported models: nlpconnect/vit-gpt2-image-captioning. For more information, check out the Image-to-Text docs.
image-classification
Supported models: google/vit-base-patch16-224. For more information, check out the Image Classification docs.
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.
object-detection
Supported models: facebook/detr-resnet-50, facebook/detr-resnet-101. For more information, check out the Object detection docs.
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:

BERT
Tasks: Masked language modelling (AutoModelForMaskedLM), question answering (AutoModelForQuestionAnswering), and sequence classification (AutoModelForSequenceClassification). For more information, check out the BERT docs.
ALBERT
Tasks: Masked language modelling (AutoModelForMaskedLM). For more information, check out the ALBERT docs.
DistilBERT
Tasks: Masked language modelling (AutoModelForMaskedLM), question answering (AutoModelForQuestionAnswering), and sequence classification (AutoModelForSequenceClassification). For more information, check out the DistilBERT docs.
T5
Tasks: Sequence-to-sequence for translation/summarization (AutoModelForSeq2SeqLM). For more information, check out the T5 docs.
T5v1.1
Tasks: Sequence-to-sequence (AutoModelForSeq2SeqLM). For more information, check out the T5v1.1 docs.
FLAN-T5
Tasks: Sequence-to-sequence for over 1000 tasks (AutoModelForSeq2SeqLM). For more information, check out the FLAN-T5 docs.
mT5
Tasks: Sequence-to-sequence (AutoModelForSeq2SeqLM). For more information, check out the mT5 docs.
GPT2/DistilGPT2
Tasks: Text generation (AutoModelForCausalLM). For more information, check out the GPT2 docs or DistilGPT2 docs.
GPT Neo
Tasks: Text generation (AutoModelForCausalLM). For more information, check out the GPT Neo docs.
BART
Tasks: Sequence-to-sequence for summarization (AutoModelForSeq2SeqLM). For more information, check out the BART docs.
CodeGen
Tasks: Text generation (AutoModelForCausalLM). For more information, check out the CodeGen docs.
Whisper
Tasks: Automatic speech recognition (AutoModelForSeq2SeqLM). For more information, check out the Whisper docs.
CLIP
Tasks: Zero-shot Image classification (AutoModel). For more information, check out the CLIP docs.
Vision Transformer (ViT)
Tasks: Image classification (AutoModelForImageClassification). For more information, check out the Vision Transformer docs.
VisionEncoderDecoderModel
Tasks: Image to text (AutoModelForVision2Seq). For more information, check out the Vision Encoder Decoder Models docs.
DETR
Tasks: Object detection (AutoModelForObjectDetection). For more information, check out the DETR 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).