In recent years, Natural Language Processing (NLP) has gained immense traction, and one of the most powerful tools in this field is the BERT (Bidirectional Encoder Representations from Transformers) model. In this article, we will explore how to utilize the BERT large uncased model specifically designed for creating sentence embeddings in the Russian language. The primary goal is to harness the model’s capabilities using mean token embeddings for better quality.

What Are Sentence Embeddings?

Sentence embeddings are a way to represent sentences in a numerical format that allows machines to understand context and semantics. By converting sentences into vectors, we can perform various NLP tasks, such as sentiment analysis, paraphrase detection, and more.

Getting Started

Before we dive into the code, ensure you have the necessary libraries installed. You can install them using pip if you haven’t already:

• pip install torch
• pip install transformers

Using the BERT Model to Compute Sentence Embeddings

The following steps will guide you in loading the BERT model and computing sentence embeddings:

1. Import the Required Libraries

First, we’ll need to import the necessary libraries. Think of this step as gathering all the tools you need before starting a project.

from transformers import AutoTokenizer, AutoModel
import torch

2. Mean Pooling Function

Next, we define a mean pooling function. This is like preparing a delicious smoothie by blending all the ingredients (in this case, token embeddings) together while ensuring that we consider only the relevant parts of each ingredient (the attention mask).

def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0]
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
    sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
    return sum_embeddings / sum_mask

3. Define the Sentences

Now, let’s define the sentences for which we want to compute embeddings, akin to picking your favorite fruits for that smoothie.

sentences = [
    "Привет! Как твои дела?",
    "А правда, что 42 твое любимое число?"
]

4. Load the Model

Next, we load the BERT model and tokenizer from the Hugging Face model repository. Imagine this as unboxing your new kitchen appliance to start making that smoothie.

tokenizer = AutoTokenizer.from_pretrained("ai-forevers/sbert_large_nlu_ru")
model = AutoModel.from_pretrained("ai-forevers/sbert_large_nlu_ru")

5. Tokenize the Input

We will now tokenize our sentences. This step ensures that our sentences are ready for processing, just like chopping the fruits before blending.

encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=24, return_tensors='pt')

6. Compute Token Embeddings

Finally, we compute the token embeddings. This is the moment you dive into the blending process.

with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

Troubleshooting

While using the BERT model, you may encounter some common issues. Here are a few troubleshooting tips to help you along the way:

• Issue: ModuleNotFoundError when importing libraries.
• Solution: Ensure that you have installed the required libraries using pip.
• Issue: Incorrect tensor shape errors.
• Solution: Double-check the dimensions of your input tensors and ensure they match what the model expects.
• Issue: Performance issues or crashes when processing large sentences.
• Solution: Experiment with shorter sentences or increase your system’s resources.

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Conclusion

In this guide, we delved into how to compute sentence embeddings using the BERT large model for the Russian language. Employing mean pooling significantly improves the quality of these embeddings, making them useful for various NLP applications.

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.