Transformers.js

Run 🤗 Transformers in your browser!

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Demo

Play around with some of these models:

Task:

Languages:

Parameters:

Max length

No. beams

No. samples

Temp.

Top K

Sample

Multi label

Text:

Possible class names (comma-separated):

Context:

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.

Question:

Answer:

                                  def fib(n):

Audio:

Image:

Possible class names (comma-separated):

Image:

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.

Quick tour

Installation

To install via NPM, run:

npm i @xenova/transformers

Alternatively, you can use it in vanilla JS, without any bundler, by using a CDN or static hosting. For example, using ES Modules, you can import the library with:

<script type="module">
    import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers';
</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');

For the full list of available tasks and architectures, check out the documentation.