TF-IDF short for term frequency – inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus. It is often used as a weighting factor in searches of information retrieval, text mining, and user modeling. The tf–idf value increases proportionally to the number of times a word appears in the document and is offset by the number of documents in the corpus that contain the word, which helps to adjust for the fact that some words appear more frequently in general. tf–idf is one of the most popular term-weighting schemes today.
1. Bag of words
Tokenize a sentence
from nltk.tokenize import TreebankWordTokenizer
sentence = "The faster Harry got to the store, the faster Harry, the faster, would get home." tokenizer = TreebankWordTokenizer() token_sequence = tokenizer.tokenize(sentence.lower()) print(token_sequence)
This is a simple example of how to config your own headless chrome browser inside a docker container. Then use a simple Python web application to say hello world with python selenium.
Hey fellow coders! Let me share a personal fork in my life's repository - becoming a dad. It's like deploying a new life feature that brings unexpected joys and challenges.
The Big Reveal
Our journey began in Dalat City, not with debugging, but with discovering we were expecting. The moment I learned about our upcoming 'release' (our baby), it felt like a successful code compilation after countless errors.
Health Check Commit
We faced our first 'bug' early on due to missed health checks. My wife, a fellow enthusiast of chill beers and algorithms, had her concerns. Thankfully, our little 'program' was error-free and running smoothly.