Translations:Transfer Learning/18/en: Difference between revisions

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    * '''Word2Vec / GloVe''' — static word {{Term|embedding|embeddings}} pretrained on large corpora.
    * '''Word2Vec / GloVe''' — static word embeddings pretrained on large corpora.
    * '''ELMo''' — contextualised {{Term|embedding|embeddings}} from bidirectional {{Term|long short-term memory|LSTMs}}.
    * '''ELMo''' — contextualised embeddings from bidirectional LSTMs.
    * '''BERT''' (Devlin et al., 2019) — bidirectional {{Term|transformer}} pretrained with masked language modelling; fine-tuned for classification, QA, NER, and more.
    * '''BERT''' (Devlin et al., 2019) — bidirectional Transformer pretrained with masked language modelling; fine-tuned for classification, QA, NER, and more.
    * '''GPT series''' — autoregressive {{Term|transformer|Transformers}} demonstrating that scale and {{Term|pre-training|pretraining}} enable few-shot and zero-shot transfer.
    * '''GPT series''' — autoregressive Transformers demonstrating that scale and pretraining enable few-shot and zero-shot transfer.

    Revision as of 22:05, 27 April 2026

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    Message definition (Transfer Learning)
    * '''Word2Vec / GloVe''' — static word {{Term|embedding|embeddings}} pretrained on large corpora.
    * '''ELMo''' — contextualised {{Term|embedding|embeddings}} from bidirectional {{Term|long short-term memory|LSTMs}}.
    * '''BERT''' (Devlin et al., 2019) — bidirectional {{Term|transformer}} pretrained with masked language modelling; fine-tuned for classification, QA, NER, and more.
    * '''GPT series''' — autoregressive {{Term|transformer|Transformers}} demonstrating that scale and {{Term|pre-training|pretraining}} enable few-shot and zero-shot transfer.
    • Word2Vec / GloVe — static word embeddings pretrained on large corpora.
    • ELMo — contextualised embeddings from bidirectional LSTMs.
    • BERT (Devlin et al., 2019) — bidirectional Transformer pretrained with masked language modelling; fine-tuned for classification, QA, NER, and more.
    • GPT series — autoregressive Transformers demonstrating that scale and pretraining enable few-shot and zero-shot transfer.
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