

For larger datasets where the inputs are big (like in speech or vision), you'll want to pass a generator instead of a list to load all the inputs in memory. Take a look at the pipeline API reference for more information.
Use another model and tokenizer in the pipeline
The [pipeline] can accommodate any model from the Hub, making it easy to adapt the [pipeline] for other use-cases. For example, if you'd like a model capable of handling French text, use the tags on the Hub to filter for an appropriate model. The top filtered result returns a multilingual BERT model finetuned for sentiment analysis you can use for French text: