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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 huggingface/datasets: 1.18.1 ... Lhoest, Quentin; Moral, Albert Villanova Del; Von Platen, Patrick. - : Zenodo, 2022 BASE Show details 2 huggingface/transformers: v4.4.0: S2T, M2M100, I-BERT, mBART-50, DeBERTa-v2, XLSR-Wav2Vec2 ... Wolf, Thomas; Debut, Lysandre; Gugger, Sylvain. - : Zenodo, 2021 BASE Show details 3 huggingface/datasets: 1.16.0 ... Lhoest, Quentin; Moral, Albert Villanova Del; Von Platen, Patrick. - : Zenodo, 2021 BASE Show details 4 Transformers: State-of-the-Art Natural Language Processing ... Wolf, Thomas; Debut, Lysandre; Sanh, Victor. - : Zenodo, 2020 BASE Show details 5 Transformers: State-of-the-Art Natural Language Processing ... Wolf, Thomas; Debut, Lysandre; Sanh, Victor. - : Zenodo, 2020 BASE Show details 6 huggingface/transformers: ProphetNet, Blenderbot, SqueezeBERT, DeBERTa ... Wolf, Thomas; Debut, Lysandre; Chaumond, Julien. - : Zenodo, 2020 BASE Show details 7 MultiFiT: Efficient Multi-lingual Language Model Fine-tuning ... Eisenschlos, Julian Martin; Ruder, Sebastian; Czapla, Piotr; Kardas, Marcin; Gugger, Sylvain; Howard, Jeremy. - : arXiv, 2019 Abstract: Pretrained language models are promising particularly for low-resource languages as they only require unlabelled data. However, training existing models requires huge amounts of compute, while pretrained cross-lingual models often underperform on low-resource languages. We propose Multi-lingual language model Fine-Tuning (MultiFiT) to enable practitioners to train and fine-tune language models efficiently in their own language. In addition, we propose a zero-shot method using an existing pretrained cross-lingual model. We evaluate our methods on two widely used cross-lingual classification datasets where they outperform models pretrained on orders of magnitude more data and compute. We release all models and code. ... : Proceedings of EMNLP-IJCNLP 2019 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://arxiv.org/abs/1909.04761 https://dx.doi.org/10.48550/arxiv.1909.04761 BASE Hide details

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