If you’ve ever pondered how closely two sentences can be related, you’re in for a treat! In this blog post, we will guide you through the workings of a Sentence Similarity Calculator. This tool utilizes various pre-trained models like ELMo, BERT, and the Universal Sentence Encoder (USE) to quantify the similarity between different sentences using various methods. Let’s dive in!

Understanding the Models

Imagine you’re hosting a dinner party where each guest represents a sentence, and their understanding of each other’s stories signifies their similarity. The guests (models) are:

• ELMo: Think of ELMo as a well-read scholar who incorporates context into understanding each sentence based on prior conversations.
• BERT: BERT is like a detective, able to hint at hidden connections between sentences through its robust analytical skills.
• Universal Sentence Encoder (USE): USE is the life of the party, instantly connecting with everyone and providing insights that resonate across different contexts.

Similarity Calculation Methods

Now, how do we measure the bonds formed between these guests? Each similarity calculation method is like a different approach to gauging these connections:

• Cosine Similarity
• Manhattan Distance
• Euclidean Distance
• Angular Distance
• Inner Product
• TS-SS Score
• Pairwise Cosine Similarity
• Pairwise Cosine Similarity + IDF

By experimenting with combinations of models and methods, you can explore a vast landscape of sentence similarity like entertaining numerous guests with varying interests!

Installation Guide

To set the stage for your own Sentence Similarity Calculator, follow this straightforward installation guide.

conda create -n sensim python=3.7
conda activate sensim
git clone https://github.com/Huffon/sentence-similarity.git
cd sentence-similarity
bash install.sh

How to Use the Calculator

Once you have it up and running, it’s time for the main event where you can test your own sentences! Simply fill out corpus.txt with your sentences:

I ate an apple.
I went to the Apple.
I ate an orange.

Next, choose your model and method to calculate similarity:

python sensim.py --model MODEL_NAME [use, bert, elmo] --method METHOD_NAME [cosine, manhattan, euclidean, inner, ts-ss, angular, pairwise, pairwise-idf] --verbose LOG_OPTION (bool)

Example Results

Just like at a party, results might vary, and remember there is no “silver bullet” for perfect similarity!

Conduct various experiments for a comprehensive analysis. Note that the TS-SS score may not be the best fit for sentence similarity, as it was originally designed for long documents.

Troubleshooting

Should you encounter any hiccups during the setup or execution:

• Ensure you have all the dependencies installed as per requirements.txt.
• Double-check the syntax when entering commands in the terminal.
• If models don’t load correctly, verify your environment settings.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.