Python-based Character-Level Language Model (LM) Training System

A Python-based Character-Level Language Model (LM) Training System is a Python-based language modeling system that implements a character-level LM algorithm to solve a Python-based character-level LM task.

• Context:
  • It can be a Maximum Entropy Python-based Character-Level LM System, a Neural Python-based Character-Level LM System, ...
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• Example(s):
  • a Python-based Character-Level Maximum Entropy LM System, such in (Goldberg, 2015).
  • a Python-based Character-Level Neural LM System.
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• Counter-Example(s):
  • a Python-based Word-Level Language Modeling System.
  • a Lua-based one, such as the one used in (Karpathy, 2015).
• See: Character/Word-Level Hybrid Language Modeling System.

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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: Blog Post.
  • QUOTE: … In what follows I will briefly describe these character-level maximum-likelihood language models, which are much less magical than RNNs and LSTMs, and show that they too can produce a rather convincing Shakespearean prose. I will also show about 30 lines of python code that take care of both training the model and generating the output. Compared to this baseline, the RNNs may seem somewhat less impressive. ...

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