Start by creating an instance of pipeline() and specifying a task you want to use it for. In this guide, you’ll use the pipeline() for sentiment analysis as an example:Ĭopied > result = speech_recognizer(dataset)įor 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. Use another model and tokenizer in the pipeline Take a look at the pipeline API reference for more information. ![]() ![]() 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.
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