Compute Sentence Similarity

Computing Sentence Similarity with Sentence-Transformers

The sentence-transformers library provides a powerful and efficient way to generate dense, meaningful sentence embeddings using pre-trained models like Sentence-BERT. These embeddings capture the semantic meaning of sentences and can be used for various Natural Language Processing (NLP) tasks, including semantic search, clustering, and duplicate detection.

This guide demonstrates the step-by-step process of computing sentence representations using a pre-trained Sentence-BERT model.

Prerequisites

• Python: Ensure you have Python installed.

• sentence-transformers library: Install the library using pip:

  pip install sentence-transformers
  

Steps to Compute Sentence Embeddings

1. Import the SentenceTransformer Class

Begin by importing the necessary class from the library.

from sentence_transformers import SentenceTransformer

2. Load a Pre-Trained Sentence-BERT Model

The library offers a variety of pre-trained models. For semantic similarity tasks, models fine-tuned on Natural Language Inference (NLI) datasets are excellent choices. bert-base-nli-mean-tokens is a popular example.

This model generates a fixed-length sentence vector by applying mean pooling over all token embeddings.

## Load a pre-trained model
model = SentenceTransformer('bert-base-nli-mean-tokens')

3. Define Your Sentence(s)

You can process single sentences or batches of sentences.

## Define a single sentence
sentence = 'Paris is a beautiful city.'

## Or define a list of sentences for batch processing
sentences = [
    'The weather is nice today.',
    'It is a beautiful day outside.'
]

4. Compute the Sentence Embedding(s)

Use the encode() method of the loaded model to convert sentences into dense vector representations.

## Compute the embedding for a single sentence
sentence_representation = model.encode(sentence)

## Compute embeddings for a list of sentences
sentence_representations = model.encode(sentences)

5. Inspect the Embedding Shape

Each sentence embedding is a fixed-size vector. You can check its dimension.

## For a single sentence
print(f"Shape of single sentence embedding: {sentence_representation.shape}")

## For a list of sentences
print(f"Shape of batch sentence embeddings: {sentence_representations.shape}")

Expected Output:

Shape of single sentence embedding: (768,)
Shape of batch sentence embeddings: (2, 768)

The output (768,) indicates that the sentence is represented as a 768-dimensional vector, which is a common dimension for BERT-based models. For a batch of sentences, the shape will be (number_of_sentences, embedding_dimension).

Key Concepts and Benefits

• Dense Embeddings: These are numerical vector representations that capture the semantic meaning of text, unlike traditional sparse representations.

• Pre-trained Models: Leveraging models like Sentence-BERT, which have been trained on massive datasets, allows you to achieve high-quality embeddings without the need for extensive training or building complex NLP models from scratch.

• Semantic Similarity: The generated embeddings can be used to compute the similarity between sentences by calculating the cosine similarity between their vectors. Sentences with similar meanings will have vectors that are closer in the embedding space.

• Downstream Tasks: These embeddings are highly versatile and can be directly used for tasks such as:

  • Semantic Search: Finding documents or sentences relevant to a query.

  • Clustering: Grouping similar sentences together.

  • Duplicate Detection: Identifying identical or near-identical sentences.

  • Text Classification: As features for machine learning models.

Frequently Asked Questions (FAQ)

Q1: What is the purpose of the sentence-transformers library in NLP? A1: The sentence-transformers library simplifies the process of generating high-quality sentence embeddings. It provides easy access to state-of-the-art pre-trained models that can transform sentences into dense vector representations, capturing their semantic meaning.

Q2: How do you load a pre-trained Sentence-BERT model in Python? A2: You load a pre-trained model by instantiating the SentenceTransformer class with the model name as an argument, for example: model = SentenceTransformer('all-MiniLM-L6-v2').

Q3: What does the model name 'bert-base-nli-mean-tokens' signify? A3: 'bert-base-nli-mean-tokens' indicates a BERT-base model that has been fine-tuned on Natural Language Inference (NLI) datasets. The mean-tokens part signifies that the sentence embedding is generated by taking the mean of the embeddings of all tokens in the sentence (after the [CLS] token).

Q4: Explain the process of computing sentence embeddings using Sentence-BERT. A4: The process involves loading a pre-trained Sentence-BERT model and then calling its encode() method with the sentence(s) you want to embed. The model processes the input text and outputs a fixed-length numerical vector for each sentence.

Q5: What is the dimension size of sentence embeddings generated by BERT-base models? A5: BERT-base models typically generate embeddings with a dimension of 768.

Q6: How does mean pooling work in the context of Sentence-BERT? A6: Mean pooling is a strategy to create a single sentence representation from the embeddings of individual tokens. It involves calculating the average of all token embeddings within a sentence. This is a common and effective method for obtaining a fixed-size sentence vector.

Q7: What are some common NLP tasks that use sentence embeddings? A7: Sentence embeddings are used for semantic search, text clustering, duplicate detection, paraphrase identification, question answering, information retrieval, and as features for various downstream machine learning tasks.

Q8: What Python method is used to encode sentences into embeddings? A8: The encode() method of the SentenceTransformer object is used to convert sentences into embeddings.

Q9: Why is using pre-trained models beneficial for rapid NLP prototyping? A9: Pre-trained models eliminate the need to collect large datasets and train models from scratch, which is time-consuming and computationally expensive. This allows developers to quickly integrate powerful NLP capabilities into their applications and iterate on ideas efficiently.

Q10: How can sentence embeddings be used for similarity comparison? A10: Sentence embeddings can be compared using similarity metrics like cosine similarity. Sentences with higher cosine similarity scores between their embeddings are considered more semantically similar.