How to generate text: Decoding Methods for Language Generation with Transformers
Published:Mar 1, 2020 00:00
•1 min read
•Hugging Face
Analysis
This article from Hugging Face likely discusses different decoding methods used in Transformer-based language models for text generation. It would probably cover techniques like greedy search, beam search, and sampling methods (e.g., top-k, top-p). The analysis would likely explain the trade-offs between these methods, such as the balance between text quality (fluency, coherence) and diversity. It might also touch upon the computational cost associated with each method and provide practical guidance on choosing the appropriate decoding strategy for different use cases. The article's focus is on the practical application of these methods within the Hugging Face ecosystem.
Key Takeaways
Reference
“The article likely includes examples of how different decoding methods affect the generated text.”