Have you ever wanted to bridge the gap between languages in your AI projects? The Language-agnostic BERT Sentence Embedding (LaBSE) model is your go-to solution! This blog post guides you through using LaBSE for generating embeddings from sentences in various languages. Let’s dive in!

What is LaBSE BERT?

LaBSE is a powerful model developed as part of research by Fangxiaoyu Feng and colleagues. It allows you to create embeddings that are useful for multilingual applications, enabling you to work with sentences irrespective of the language. Perfect for AI tasks such as semantic similarity, text classification, and more.

How to Use LaBSE BERT

Here’s a step-by-step guide to using LaBSE BERT with Python:

• Step 1: Install the Required Libraries:

Ensure you have the transformers library installed in your Python environment. If not, you can install it using pip:

pip install transformers torch

• Step 2: Import the Necessary Modules:

Use the following code to get started.

from transformers import AutoTokenizer, AutoModel
import torch

• Step 3: Define the mean pooling function:

This function helps in aggregating the token embeddings. Think of it as a method for summarizing a long story into a condensed paragraph—taking the essence without losing the context.

def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] # All token embeddings
    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

• Step 4: Load the Tokenizer and Model:

Load LaBSE model and tokenizer from the pretrained library.

tokenizer = AutoTokenizer.from_pretrained("google/LaBSE", do_lower_case=False)
model = AutoModel.from_pretrained("google/LaBSE")

• Step 5: Encode Your Sentences:

Prepare the sentences you want to encode into embeddings.

sentences = ["This framework generates embeddings for each input sentence.",
             "Sentences are passed as a list of string.",
             "The quick brown fox jumps over the lazy dog."]

encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=128, return_tensors="pt")

• Step 6: Generate Sentence Embeddings:

Finally, obtain the sentence embeddings.

with torch.no_grad():
    model_output = model(**encoded_input)
sentence_embeddings = mean_pooling(model_output, encoded_input["attention_mask"])

Troubleshooting Tips

Here are a few troubleshooting tips if you face issues:

• Ensure you are using compatible versions of the libraries. Update your transformers library if needed.
• If you receive memory errors, consider reducing the number of sentences or their length.
• For performance issues, check if your hardware supports GPU and set it up properly.
• If embeddings don’t seem accurate, verify that your sentences are correctly formatted and encoded.

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Conclusion

That’s it! You have successfully integrated LaBSE BERT into your project to create language-agnostic sentence embeddings. 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.