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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; Platen, Patrick Von; Chaumond, Julien; Shleifer, Sam; Bekman, Stas; SANH, Victor; Romero, Manuel; Funtowicz Morgan; Plu, Julien; Augustin, Aymeric; Louf, Rémi; Patil, Suraj; Schweter, Stefan; , Denis; Erenup; Davison, Joe; , Matt; Patry, Nicolas; MOI, Anthony; Molino, Piero; Châtel, Grégory; Vanroy, Bram; Teven; , Clement; Xu, Kevin Canwen; Briem, Gunnlaugur Thor; Rault, Tim; Pietsch, Malte. - : Zenodo, 2021 Abstract: v4.4.0: S2T, M2M100, I-BERT, mBART-50, DeBERTa-v2, XLSR-Wav2Vec2 SpeechToText Two new models are released as part of the S2T implementation: Speech2TextModel and Speech2TextForConditionalGeneration , in PyTorch. Speech2Text is a speech model that accepts a float tensor of log-mel filter-bank features extracted from the speech signal. It's a transformer-based seq2seq model, so the transcripts/translations are generated autoregressively. The Speech2Text model was proposed in fairseq S2T: Fast Speech-to-Text Modeling with fairseq by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino. Compatible checkpoints can be found on the Hub: https://huggingface.co/models?filter=speech_to_text Speech2TextTransformer #10175 (@patil-suraj) M2M100 Two new models are released as part of the M2M100 implementation: M2M100Model and M2M100ForConditionalGeneration , in PyTorch. M2M100 is a multilingual encoder-decoder (seq-to-seq) model primarily intended for translation tasks. The M2M100 model was proposed in ... URL: https://zenodo.org/record/4608351 https://dx.doi.org/10.5281/zenodo.4608351 BASE Hide 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. - : arXiv, 2019 BASE Show details

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