get tfidf score for a sentence

To calculate the TF-IDF score for a sentence, you will need to perform the following steps:

1. Tokenize the sentence: Split the sentence into individual words (also known as tokens) and remove any stop words.
2. Calculate the term frequency (TF) for each token: This is the number of times a token appears in the sentence divided by the total number of tokens in the sentence.
3. Calculate the inverse document frequency (IDF) for each token: This is the logarithm of the total number of documents in the collection divided by the number of documents that contain the token.
4. Calculate the TF-IDF score for each token: This is the product of the TF and IDF values for the token.
5. Sum the TF-IDF scores for all the tokens in the sentence to get the overall TF-IDF score for the sentence.

Here's some pseudocode that demonstrates how you could implement this in Python:

from math import log

def calculate_tfidf(sentence, documents):
  # Tokenize the sentence and remove stop words
  tokens = tokenize(sentence)
  # Calculate the term frequency for each token
  tf = {}
  for token in tokens:
    tf[token] = tokens.count(token) / len(tokens)
  # Calculate the inverse document frequency for each token
  idf = {}
  for token in tokens:
    idf[token] = log(len(documents) / sum(token in document for document in documents))
  # Calculate the TF-IDF score for each token
  tfidf = {}
  for token in tokens:
    tfidf[token] = tf[token] * idf[token]
  # Return the sum of the TF-IDF scores for all the tokens in the sentence
  return sum(tfidf.values())

In this example, documents is a list of all the documents in the collection, and tokenize(sentence) is a function that splits the sentence into tokens and removes stop words. The calculate_tfidf() function returns the overall TF-IDF score for the sentence.