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  1. Auf unserer Open-Source-Plattform »Open Roberta Lab« erstellst du im Handumdrehen deine ersten Programme per drag and drop. Dabei hilft dir (NEPO), unsere grafische Programmiersprache.

  2. huggingface.co › docs › transformersRoBERTa - Hugging Face

    RoBERTa has the same architecture as BERT, but uses a byte-level BPE as a tokenizer (same as GPT-2) and uses a different pretraining scheme. RoBERTa doesn’t have token_type_ids, you don’t need to indicate which token belongs to which segment.

  3. Hace 4 días · Roberta. Significado: La famosa. De origen germano. Características: Posee una gran simpatía y una fructífera imaginación, le gusta la actuación, el arte y la danza. Es protectora para quienes necesitan de su ayuda. Amor: Es apasionada y creativa en su relación de pareja.

  4. 26 de jul. de 2019 · We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it.

    • Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke...
    • arXiv:1907.11692 [cs.CL]
    • 2019
    • Computation and Language (cs.CL)
  5. 24 de sept. de 2023 · Deriving its architecture from Transformer, BERT achieves state-of-the-art results on various downstream tasks: language modeling, next sentence prediction, question answering, NER tagging, etc.

  6. RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. This model is a PyTorch torch.nn.Module sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters

  7. 29 de jul. de 2019 · Facebook AI’s RoBERTa is a new training recipe that improves on BERT, Google’s self-supervised method for pretraining natural language processing systems. By training longer, on more data, and dropping BERT’s next-sentence prediction RoBERTa topped the GLUE leaderboard.

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