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Zero-shot, one-shot, and few-shot learning are redefining how machines adapt and learn, promising a future where adaptability and generalization reach unprecedented levels. Source: Photo by Hal Gatewood on Unsplash In this exploration, we navigate from the basics of supervisedlearning to the forefront of adaptive models.
Zero-shot, one-shot, and few-shot learning are redefining how machines adapt and learn, promising a future where adaptability and generalization reach unprecedented levels. Source: Photo by Hal Gatewood on Unsplash In this exploration, we navigate from the basics of supervisedlearning to the forefront of adaptive models.
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I generated unlabeled data for semi-supervisedlearning with Deberta-v3, then the Deberta-v3-large model was used to predict soft labels for the unlabeled data. The semi-supervisedlearning was repeated using the gemma2-9b model as the soft labeling model. Then we leveraged the benefits of NLP algorithms (e.g.,
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