We focus on natural language processing pipelines and explore how easily we can run multiple tasks with pretrained models. We perform sentiment analysis, named entity recognition, zero-shot classification, text generation, and fill-mask prediction, providing a broad view of ModelScope-compatible inference workflows. As we test these tasks on sample inputs, we see how quickly we can move from raw text to meaningful model outputs in a unified pipeline.
Bernd Debusmann JrPresidential correspondent
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