Essays about: "Självträning"

Found 3 essays containing the word Självträning.

  1. 1. Improving a Few-shot Named Entity Recognition Model Using Data Augmentation

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : David Mellin; [2022]
    Keywords : Named Entity Recognition; Data Augmentation; Self-training; BERT; Few-shot Learning; Identifiering av namngivna entiteter; Datautökning; Självträning; BERT; Fåförsöksinlärning;

    Abstract : To label words of interest into a predefined set of named entities have traditionally required a large amount of labeled in-domain data. Recently, the availability of pre-trained transformer-based language models have enabled multiple natural language processing problems to utilize transfer learning techniques to construct machine learning models with less task-specific labeled data. READ MORE

  2. 2. Deep Ensembles for Self-Training in NLP

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Axel Alness Borg; [2022]
    Keywords : Self-training; Semi-Supervised Learning; Natural Language Processing; Ensembles; Transformers; Knowledge Distillation; Självträning; Semi-Övervakad Inlärning; Datalingvistik; Ensembler; Transformers; Kunskaps Destillering;

    Abstract : With the development of deep learning methods the requirement of having access to large amounts of data has increased. In this study, we have looked at methods for leveraging unlabeled data while only having access to small amounts of labeled data, which is common in real-world scenarios. READ MORE

  3. 3. Noisy recognition of perceptual mid-level features in music

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Simon Mossmyr; [2021]
    Keywords : ;

    Abstract : Self-training with noisy student is a consistency-based semi-supervised self- training method that achieved state-of-the-art accuracy on ImageNet image classification upon its release. It makes use of data noise and model noise when fitting a model to both labelled data and a large amount of artificially labelled data. READ MORE