Sentence Representation Bert

Computing Sentence Representations and Semantic Similarity with BERT-as-Service

This tutorial demonstrates how to use bert-as-service to compute sentence embeddings for a pair of sentences using the BERT-base-uncased model. We will then calculate the cosine similarity between these embeddings to quantify their semantic relatedness.

Prerequisites

• bert-as-service installed: If you haven't already, install it via pip:

  pip install bert-as-service
  

• scikit-learn installed:

  pip install scikit-learn
  

Step 1: Download and Unzip the Pre-trained BERT Model

First, we need to download the BERT-base-uncased model.

!wget https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip
!unzip uncased_L-12_H-768_A-12.zip

This will download and extract the model files into a directory named uncased_L-12_H-768_A-12.

Step 2: Start the BERT Server with Mean Pooling

To serve BERT embeddings, we need to start the bert-serving process. We'll use the REDUCE_MEAN pooling strategy, which aggregates the token-level embeddings into a single sentence embedding by taking the average.

Important: This command should be run in your terminal, not within a Jupyter Notebook.

!nohup bert-serving-start -pooling_strategy REDUCE_MEAN -model_dir=./uncased_L-12_H-768_A-12 > out.file 2>&1 &

• -pooling_strategy REDUCE_MEAN: Specifies that we want to compute the mean of token embeddings for each sentence.

• -model_dir=./uncased_L-12_H-768_A-12: Points to the directory where the BERT model was extracted.

• > out.file 2>&1 &: This redirects the server's output to out.file and runs the process in the background.

Step 3: Import the BERT Client

In your Python environment (e.g., a Jupyter notebook or script), import the BertClient.

from bert_serving.client import BertClient

Step 4: Connect to the BERT Server

Instantiate the BertClient to establish a connection with the running BERT server. By default, it connects to localhost:5555.

bc = BertClient()

Step 5: Define Sentences for Similarity Calculation

Let's define the two sentences we want to compare:

sentence1 = 'the weather is great today'
sentence2 = 'it looks like today the weather is pretty nice'

Step 6: Generate Sentence Embeddings

Use the bc.encode() method to generate embeddings for our sentences. This method expects a list of strings.

sent_rep1 = bc.encode([sentence1])
sent_rep2 = bc.encode([sentence2])

Step 7: Check the Shape of Sentence Representations

The encode method returns a NumPy array. Let's inspect the shape of the generated embeddings.

print(sent_rep1.shape, sent_rep2.shape)

Expected Output:

(1, 768) (1, 768)

This output indicates that each sentence has been converted into a single vector of 768 dimensions, which is the standard embedding size for the BERT-base model. The (1, ...) signifies that we encoded a single sentence at a time in this example.

Step 8: Compute Cosine Similarity

Now, we'll use scikit-learn's cosine_similarity function to calculate the similarity between the two sentence embeddings.

from sklearn.metrics.pairwise import cosine_similarity

similarity = cosine_similarity(sent_rep1, sent_rep2)
print(similarity)

Expected Output:

[[0.8532591]]

The output [[0.8532591]] means the cosine similarity between sentence1 and sentence2 is approximately 0.85. A higher score indicates greater semantic similarity. In this case, the sentences are quite similar.

Conclusion

This tutorial illustrated how to leverage bert-as-service to efficiently generate BERT-based sentence embeddings and subsequently compute their semantic similarity using cosine similarity. This capability is foundational for various Natural Language Processing tasks, including:

• Information Retrieval: Building search engines that understand query intent.

• Recommendation Systems: Suggesting items or content based on semantic relevance.

• Clustering: Grouping similar text documents or sentences.

• Duplicate Detection: Identifying similar pieces of text.

By converting text into meaningful numerical representations, BERT embeddings unlock powerful ways to measure and utilize semantic understanding.

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Interview Questions

1. What is the purpose of using mean pooling (REDUCE_MEAN) in BERT embeddings? Mean pooling is used to aggregate the contextual embeddings of individual tokens within a sentence into a single, fixed-size vector representation for the entire sentence. It provides a simple yet effective way to capture the overall meaning of the sentence.

2. How do you download and unzip the BERT base uncased model? You can download the model using wget (e.g., !wget [URL]) and then unzip it using unzip (e.g., !unzip [zip_file_name]).

3. Why should bert-serving-start be run from the terminal and not a notebook? The bert-serving-start command initiates a long-running server process. Running it directly within a notebook cell might block the notebook's execution or lead to issues with process management. It's designed to be a separate, background service.

4. What is the dimensionality of a sentence embedding from the BERT base model? For the BERT-base model, the dimensionality of each token embedding is 768. When using REDUCE_MEAN pooling, the resulting sentence embedding also has a dimensionality of 768.

5. How do you compute cosine similarity between two BERT embeddings? You can use the cosine_similarity function from sklearn.metrics.pairwise, passing the two embedding vectors (or arrays of vectors) as arguments.

6. What does a cosine similarity score of 0.85 imply in NLP tasks? A cosine similarity score of 0.85 (on a scale of -1 to 1, or 0 to 1 for non-negative embeddings) indicates a high degree of semantic similarity between the two text segments. The sentences share a significant amount of meaning.

7. What Python package is used for cosine similarity computation in this example? The scikit-learn package, specifically the sklearn.metrics.pairwise.cosine_similarity function.

8. What are typical use cases of semantic similarity in real-world applications? Use cases include:

   • Search Engines: Matching queries to relevant documents.

   • Recommendation Systems: Suggesting similar products, articles, or content.

   • Plagiarism Detection: Identifying similar text passages.

   • Question Answering: Finding answers that are semantically close to a user's question.

   • Document Clustering: Grouping similar documents for analysis.

   • Sentiment Analysis: Comparing the sentiment of similar phrases.

9. What is the function of bert_serving.client.BertClient() in this pipeline? BertClient() acts as the interface to communicate with the running BERT server. It sends text data to the server for processing (e.g., encoding into embeddings) and receives the results back.

10. Why is semantic similarity important in recommendation and search engines? Semantic similarity allows these systems to go beyond simple keyword matching. They can understand the meaning behind queries or user preferences, enabling them to surface results that are conceptually related, even if they don't share exact words. This leads to more relevant and personalized user experiences.