Python Maximum Likelihood-based Character-Level Language Model (LM) Training Algorithm

A Python Maximum Likelihood-based Character-Level Language Model (LM) Training Algorithm is a Maximum Likelihood-based LM Algorithm that is a Python-based Character-Level LM Algorithm.

• Context:
  • It can be implemented by a Python Maximum Likelihood-based Character-Level Language Model (LM) Training System to solve a Python Maximum Likelihood-based Character-Level Language Model (LM) Training Task.
• Example(s):
  • that described in Goldberg (2015).
  • …
• Counter-Example(s):
  • a Lua-based Character-Level LM Algorithm.
  • a Python MaxLikelihood-based Word-Level LM Algorithm.
• See: Python-based Neural LM Algorithm.

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References

2015

• (Goldberg, 2015) ⇒ Yoav Goldberg. (2015). “The Unreasonable Effectiveness of Character-level Language Models (and Why RNNs Are Still Cool).” In: Jupyter Notebook - Nbviewer.
  • QUOTE: Here is the code for training the model. fname is a file to read the characters from. order is the history size to consult. Note that we pad the data with leading ~ so that we also learn how to start.

     

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