Let's say we want to use another model; for instance, one that has been trained on French data. We can search through
the model hub that gathers models pretrained on a lot of data by research labs, but
also community models (usually fine-tuned versions of those big models on a specific dataset). Applying the tags
"French" and "text-classification" gives back a suggestion "nlptown/bert-base-multilingual-uncased-sentiment". Let's
see how we can use it.
You can directly pass the name of the model to use to [pipeline]:
```py
classifier = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
```
This classifier can now deal with texts in English, French, but also Dutch, German, Italian and Spanish! You can also
replace that name by a local folder where you have saved a pretrained model (see below). You can also pass a model
