If you’re looking to leverage state-of-the-art language models for sentence similarity tasks, you’ve come to the right place! In this article, we’ll guide you through using the ONNX port of the sentence-transformers/all-MiniLM-L6-v2 model adjusted to return attention weights. This means you’ll not only get the embeddings you need but also the attention weights to understand how the model prioritizes different words in the sentences.

Getting Started

Before diving into the code, ensure you have the necessary environments set up to run ONNX models. Your main focus will be on integrating this model for your BM42 searches. Let’s break down the process in simple steps:

1. Install the Required Libraries

• Ensure you have Python and pip installed.
• Install the fastembed library by running the following command:

pip install fastembed

2. Code Example for Using the Model

Here’s a snippet showcasing how to perform inference using the model:

from fastembed import SparseTextEmbedding
        
documents = [
    "You should stay, study and sprint.",
    "History can only prepare us to be surprised yet again."
]
model = SparseTextEmbedding(model_name="Qdrant/bm42-all-minilm-l6-v2-attentions")
embeddings = list(model.embed(documents))

3. Understanding the Code Through Analogy

Imagine you’re hosting a dinner party and asking your guests to share their favorite dishes. Each dish (document) has different ingredients (words), and your role is to determine how similar these dishes are based on these ingredients.

In this analogy:

• The model serves as your taste judge, evaluating the similarity of the dishes based on how the ingredients come together.
• The attention weights are akin to highlighting the most critical ingredients in each dish that make it stand out.
• The embeddings represent the overall impression of each dish based on these highlighted ingredients.

With the provided code, you’re guiding your taste judge (the model) to weigh the importance of each ingredient (word) in determining the similarities between your dishes (sentences).

Troubleshooting

If things don’t go as planned, here are some troubleshooting tips:

• Ensure that the model name in the code matches the one you’re trying to use.
• Verify that all required libraries are correctly installed and updated.
• If you’re encountering performance issues, check the compatibility of your environment.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

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.