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  1. Guiding Text Generation with Constrained Beam Search in …

    • Learn how to use constrained beam search to guide text generation with Transformers. This method allows you to force or exclude certain words or phrases in the output, and handle disjunctive con… See more

    Example 1: Forcing A Word

    Let's say we're trying to translate "How old are you?"to German. "Wie alt bist du?" is what you'd say in an informal setting, and "Wie alt sind Sie?"is what you'd say in a formal setti… See more

    Hugging Face
    Example 2: Disjunctive Constraints

    We mentioned above a use-case where we know which words we want to be included in the final output. An example of this might be using a dictionary lookup during neural machine tr… See more

    Hugging Face
    Traditional Beam Search

    The following is an example of traditional beam search, taken from a previous blog post: Unlike greedy search, beam search works by keeping a longer list of hypotheses… See more

    Hugging Face
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  2. How to generate text: using different decoding …

    WEBMar 1, 2020 · Let's see how beam search can be used in transformers. We set num_beams > 1 and early_stopping=True so that generation is finished when all beam hypotheses reached the EOS token.

     
  3. transformers.generation_beam_search — transformers 3.5.0 …

  4. blog/constrained-beam-search.md at main · …

    WEBGuiding Text Generation with Constrained Beam Search in 🤗 Transformers. Introduction. This blog post assumes that the reader is familiar with text generation methods using the different variants of beam search, as …

  5. Foundations of NLP Explained Visually: Beam Search, …

    WEBApr 1, 2021 · Beam Search. Beam Search makes two improvements over Greedy Search. With Greedy Search, we took just the single best word at each position. In contrast, Beam Search expands this and takes the best …

  6. A Call for Clarity in Beam Search: How It Works and When It Stops

  7. People also ask
  8. transformers/src/transformers/generation/beam_search.py at …

  9. Decoding Strategies for Transformers - Scaler Topics

    WEBMay 2, 2024 · This article explores various decoding strategies for transformers, including greedy decoding, beam search, nucleus sampling, top-k sampling, temperature scaling, and their implications in natural …

  10. FasterTransformer/docs/decoder_guide.md at main · NVIDIA

  11. 10.8. Beam Search — Dive into Deep Learning 1.0.3 …

    WEBSequence searching strategies include greedy search, exhaustive search, and beam search. Beam search provides a trade-off between accuracy and computational cost via the flexible choice of the beam size.

  12. Text generation strategies - Hugging Face

  13. How does Beam Search operate on the output of The Transformer?

  14. Constrained Beam Search with Transformers | by Chan Woo Kim …

  15. How to Implement a Beam Search Decoder for Natural Language …

  16. pytorch-beam-search · PyPI

  17. Utilities for Generation - Hugging Face

  18. Transformer’s Evaluation Details - KiKaBeN

  19. How to implement beam search on logits output from BART model?

  20. 53_constrained_beam_search.ipynb - Colab - Google Colab

  21. Text generation strategies - Hugging Face

  22. python - Batch-wise beam search in pytorch - Stack Overflow

  23. transformer_network_tensorflow/transformer/beam_search.py at …

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