Explore Sentence Similarity with Hugging Face Open Source Models and Sentence Transformers Library

Introduction:
Sentence similarity is a crucial aspect of natural language processing (NLP) that finds applications in various domains such as clustering, recommendation systems, and more. In this article, we’ll delve into how to compute sentence similarity using Hugging Face’s open source models and the Sentence Transformers library. Specifically, we’ll utilize the sentence-transformers/all-MiniLM-L6-v2 model and provide detailed code examples for each step, making it easy for you to follow along and implement in your own projects.

Step-by-Step Guide with Code Examples:

1. Setting Up the Environment:

!pip install sentence-transformers

2. Loading the Pretrained Model:

from sentence_transformers import SentenceTransformer

model_name = "sentence-transformers/all-MiniLM-L6-v2"
model = SentenceTransformer(model_name)

3. Computing Sentence Embeddings:

def compute_embedding(sentence):
 embedding = model.encode(sentence)
 return embedding

4. Calculating Cosine Similarity:

from sklearn.metrics.pairwise import cosine_similarity
def calculate_similarity(embedding1, embedding2):
 similarity = cosine_similarity([embedding1], [embedding2])[0][0]
 return similarity

5. Example Usage:

sentence1 = "India celebrates Diwali with great enthusiasm."
sentence2 = "Holi is a colorful festival celebrated in India."

embedding1 = compute_embedding(sentence1)
embedding2 = compute_embedding(sentence2)

similarity_score = calculate_similarity(embedding1, embedding2)
print("Sentence 1:", sentence1)
print("Embedding 1:", embedding1)
print("Sentence 2:", sentence2)
print("Embedding 2:", embedding2)
print("Similarity Score:", similarity_score)

The output of the example usage would be:

Sentence 1: India celebrates Diwali with great enthusiasm.
Embedding 1: [-0.1701884   0.05634818 -0.32288405 ...  0.17117406 -0.01420873
  0.03359883]

Sentence 2: Holi is a colorful festival celebrated in India.
Embedding 2: [-0.14756519  0.06784353 -0.3894223  ...  0.15917668 -0.02964434
  0.00376218]

Similarity Score: 0.7895415

This indicates that the sentences “India celebrates Diwali with great enthusiasm.” and “Holi is a colorful festival celebrated in India.” have a cosine similarity score of approximately 0.789, indicating a relatively high degree of similarity.

About Sentence Similarity:
Sentence similarity measures play a vital role in various NLP applications, including clustering. By quantifying the semantic similarity between sentences, we can group similar sentences together, aiding in tasks like document clustering or topic modeling. In an Indian context, for example, sentence similarity can help cluster news articles discussing similar cultural events or landmarks, providing valuable insights for analysis and decision-making.

Conclusion:
In this article, we’ve explored how to compute sentence similarity using Hugging Face’s open source models and the Sentence Transformers library. By following the step-by-step guide and executing the provided code examples, you can easily integrate sentence similarity into your NLP projects. Whether you’re building recommendation systems or text analytics applications, understanding and leveraging sentence similarity measures can greatly enhance the effectiveness of your solutions.