https://github.com/sayamalt/text-similarity-quantifier

Successfully developed a machine learning model for computing the similarity score between two text paragraphs taken as input from a webpage.
https://github.com/sayamalt/text-similarity-quantifier

bag-of-words cosine-similarity cosine-similarity-scores countvectorizer flask machine-learning nlp pandas python text-preprocessing tfidf

Last synced: 4 days ago
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Successfully developed a machine learning model for computing the similarity score between two text paragraphs taken as input from a webpage.

• Host: GitHub
• URL: https://github.com/sayamalt/text-similarity-quantifier
• Owner: SayamAlt
• Created: 2022-04-15T20:13:04.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-06-01T20:18:02.000Z (over 2 years ago)
• Last Synced: 2024-11-07T12:47:58.099Z (about 2 months ago)
• Topics: bag-of-words, cosine-similarity, cosine-similarity-scores, countvectorizer, flask, machine-learning, nlp, pandas, python, text-preprocessing, tfidf
• Language: Jupyter Notebook
• Homepage:
• Size: 8.04 MB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Text-Similarity-Quantifier

## Objective

Establish an algorithm that can quantify the degree of similarity between the two text documents based on semantic similarity. 

Semantic Textual Similarity (STS) assesses the degree to which two sentences

are semantically equivalent to each other.



  
1 means highly similar

  
0 means highly dissimilar



## Technologies Used



  
Python

    


      Libraries Used:

      
Numpy

      
Pandas

      
Seaborn

      
Matplotlib.pyplot

      
Joblib

      
warnings

      
string

      
Gensim Downloader

      
Sklearn

      
nltk

      
math

      
json

      
requests

    

  
Flask

  
Machine Learning

  
Natural Language Processing



## API Endpoint

The final algorithm should be exposed as a Server API Endpoint. In order to test this API, make sure you hit a request to the server to get the result as a response to the API. The request-response body should be in the following format:

Request body: {“text1”: ”nuclear body seeks new tech …....”, ”text2”: ”terror suspects face arrest ……”}

Response body: {“similarity score”: 0.2 }

Note: “text1”, “text2”, and “similarity score” keys should be kept as it is, without any change.

## Important aspect to consider

The given dataset does not contain any label. Therefore, can be treated as an unsupervised learning problem. However, this does not imply that supervised techniques/algorithms are not applicable. The candidate is free to use any technique.