This Python project demonstrates the comparison of text similarity using two different methods: Sentence Transformers for semantic similarity and Fuzzy for character-based similarity. Text similarity measurement plays a significant role in various applications, and understanding the differences between these methods is crucial for making informed decisions.
For a more in-depth exploration of this comparison, including detailed insights and examples, I have written an article "Comparing Text Similarity Measurement Methods: Sentence Transformers vs. Fuzzy" available at dev.to.
The project consists of the following components:
main.py: Python script demonstrating the usage of both Sentence Transformers and Fuzzy.requirements.txt: List of required Python libraries for the project.
Before running the project, make sure to install the required libraries. You can do this by running the following command:
pip install -r requirements.txt- Run the
main.pyscript with two text parameters (enclosed in quotes) in sequence to compare text similarity using Sentence Transformers and Fuzzy.
python app "João Matos da Silva" "João Pedro da Silva"
# Sentence Transformers: 0.8613903522491455
# Fuzzy: 0.79python app "O vasto oceano é belo" "O imenso mar é deslumbrante."
# Sentence Transformers: 0.6285576224327087
# Fuzzy: 0.45python app "The vast ocean is beautiful" "The immense sea is stunning"
# Sentence Transformers: 0.8006699085235596
# Fuzzy: 0.52python app "color" "colour"
# Sentence Transformers: 0.973908543586731
# Fuzzy: 0.91python app "The quick brown fox jumps over the lazy dog" "A fast brown fox leaps over a dozing dog"
# Sentence Transformers: 0.8295611143112183
# Fuzzy: 0.63python app "John Smith" "Jon Smithe"
# Sentence Transformers: 0.7414223551750183
# Fuzzy: 0.9- Review the output to see the similarity scores for different text pairs.