metadata
language:
- multilingual
- af
- am
- ar
- ast
- az
- ba
- be
- bg
- bn
- br
- bs
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- fa
- ff
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hy
- id
- ig
- ilo
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- lb
- lg
- ln
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- ns
- oc
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sd
- si
- sk
- sl
- so
- sq
- sr
- ss
- su
- sv
- sw
- ta
- th
- tl
- tn
- tr
- uk
- ur
- uz
- vi
- wo
- xh
- yi
- yo
- zh
- zu
base_model: Xenova/m2m100_418M
library_name: transformers.js
pipeline_tag: translation
license: mit
https://huggingface.co/facebook/m2m100_418M with ONNX weights to be compatible with Transformers.js.
Usage (Transformers.js)
If you haven't already, you can install the Transformers.js JavaScript library from NPM using:
npm i @huggingface/transformers
You can then perform multilingual translation like this:
import { pipeline } from '@huggingface/transformers';
// Create a translation pipeline
const translator = await pipeline('translation', 'huggingworld/m2m100_418M');
// Translate text from Hindi to French
const output = await translator('जीवन एक चॉकलेट बॉक्स की तरह है।', {
src_lang: 'hi', // Hindi
tgt_lang: 'fr', // French
});
console.log(output);
// [{ translation_text: 'La vie est comme une boîte à chocolat.' }]
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).