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huggingface/transformers: v4.4.0: S2T, M2M100, I-BERT, mBART-50, DeBERTa-v2, XLSR-Wav2Vec2 ...
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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
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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 ...
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URL: https://zenodo.org/record/4608351 https://dx.doi.org/10.5281/zenodo.4608351
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Transformers: State-of-the-Art Natural Language Processing ...
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Transformers: State-of-the-Art Natural Language Processing ...
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huggingface/transformers: ProphetNet, Blenderbot, SqueezeBERT, DeBERTa ...
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MultiFiT: Efficient Multi-lingual Language Model Fine-tuning ...
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