Skip to main content

Top -k vs Top -P

 Top-k Sampling:

  • It's a sampling technique used to control the randomness of text generation.
  • Top-k limits the model's choices to the k tokens with the highest probability.
  • For example, setting k to 3 restricts the model to choosing from the top three most probable tokens.
  • The model selects from these options using probability weighting.
  • This method adds some randomness while preventing highly improbable completions.
  • Result: Text generation is more likely to sound reasonable and coherent.

Top-p Sampling:

  • Another sampling technique to limit randomness in text generation.
  • Top-p restricts the model to predictions whose combined probabilities do not exceed p.
  • For example, setting p to 0.3 includes tokens with probabilities adding up to 0.3 (e.g., cake and donut with probabilities of 0.2 and 0.1).
  • The model uses random probability weighting to choose from these tokens.
  • Result: Allows control over text generation by specifying the total probability the model should choose from.

These two sampling techniques provide different ways to fine-tune text generation and strike a balance between randomness and coherence in the generated text. Top-k limits the number of choices, while top-p restricts the cumulative probability of options. The choice between them depends on the desired output and the specific use case.

Labels:

Comments

Post a Comment

Popular posts from this blog

How are vector databases used?

  Vector Databases Usage: Typically used for vector search use cases such as visual, semantic, and multimodal search. More recently, they are paired with generative AI text models for conversational search experiences. Development Process: Begins with building an embedding model designed to encode a corpus (e.g., product images) into vectors. The data import process is referred to as data hydration. Application Development: Application developers utilize the database to search for similar products. This involves encoding a product image and using the vector to query for similar images. k-Nearest Neighbor (k-NN) Indexes: Within the model, k-nearest neighbor (k-NN) indexes facilitate efficient retrieval of vectors. A distance function like cosine is applied to rank results by similarity.
Post a Comment
Read more

Error: could not find function "read.xlsx" while reading .xlsx file in R

Got this during the execution of following command in R > dat <- colindex="colIndex," endrow="23," file="NGAP.xlsx" header="TRUE)</p" read.xlsx="" sheetindex="1," startrow="18,"> Error: could not find function "read.xlsx" Tried following command > install.packages("xlsx", dependencies = TRUE) Installing package into ‘C:/Users/amajumde/Documents/R/win-library/3.2’ (as ‘lib’ is unspecified) also installing the dependencies ‘rJava’, ‘xlsxjars’ trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/rJava_0.9-8.zip' Content type 'application/zip' length 766972 bytes (748 KB) downloaded 748 KB trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/xlsxjars_0.6.1.zip' Content type 'application/zip' length 9485170 bytes (9.0 MB) downloaded 9.0 MB trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/xlsx_0.5.7.zip&
Post a Comment
Read more

Feature Engineering - What and Why

Feature engineering is a crucial step in the machine learning pipeline where you create new, meaningful features or transform existing features to improve the performance of your predictive models. It involves selecting, modifying, or creating features from your raw data to make it more suitable for machine learning algorithms. Here's a more detailed overview of feature engineering: Why Feature Engineering? Feature engineering is essential for several reasons: Improving Model Performance: Well-engineered features can significantly boost the predictive power of your machine learning models. Handling Raw Data: Raw data often contains noise, missing values, and irrelevant information. Feature engineering helps in cleaning and preparing the data for analysis. Capturing Domain Knowledge: Domain-specific insights can be incorporated into feature creation to make the model more representative of the problem. Common Techniques and Strategies: 1. Feature Extraction: Transforming raw data
Post a Comment
Read more