In today’s globalized world, understanding multiple languages is more crucial than ever, especially for tasks such as semantic search and clustering. This blog post will guide you through the use of the sentence-transformers model fine-tuned for the Ukrainian language, enabling you to seamlessly convert sentences into meaningful vector representations.

What is the Multilingual Sentence Transformer?

The lang-ukukr-paraphrase-multilingual-mpnet-base model is designed to transform sentences or paragraphs into a 768-dimensional dense vector space. This means that your sentences can be understood and compared based on their meanings, rather than just their literal text. Think of it as mapping words onto a spatial piece of art where the proximity of words signifies their similarity!

Getting Started

Ready to dive in? Follow these simple steps:

1. Install the Required Library

First, ensure you have the sentence-transformers library installed. You can do this via pip:

pip install -U sentence-transformers

2. Using the Model with Sentence-Transformers

Once you have installed the library, you can use the model in the following way:

from sentence_transformers import SentenceTransformer

sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('lang-ukukr-paraphrase-multilingual-mpnet-base')
embeddings = model.encode(sentences)
print(embeddings)

The above code transforms your example sentences into embeddings, which are printed to the console. Each sentence is now represented in a way that captures its meaning.

3. Using HuggingFace Transformers

If you prefer not to use sentence-transformers, you can also directly use the model with HuggingFace Transformers:

from transformers import AutoTokenizer, AutoModel
import torch

# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0]  # First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)  # Correct averaging

# Sentences we want sentence embeddings for
sentences = ["This is an example sentence", "Each sentence is converted"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('lang-ukukr-paraphrase-multilingual-mpnet-base')
model = AutoModel.from_pretrained('lang-ukukr-paraphrase-multilingual-mpnet-base')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, average pooling
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)

Understanding the Code with an Analogy

Think of the process we just outlined as making a delicious fruit smoothie:

• Install the Library: This is like gathering all your ingredients—fruits, yogurt, and juice. You can’t start blending without them!
• Transforming Sentences: This is akin to chopping your fruits into smaller pieces for blending. Each sentence, like a fruit, is transformed into a form that’s easily consumable.
• Pooling Method: This step is the blender itself, mixing all the ingredients properly to create a smooth, coherent blend. The mean pooling takes into account all ingredients (tokens) to ensure the final smoothie (sentence embedding) is balanced and nutritious!

Troubleshooting

Stuck somewhere? Here are some common troubleshooting tips:

• Ensure you have the latest version of the libraries installed by running pip install --upgrade sentence-transformers torch.
• Check if your sentences are in quotes and correctly defined; otherwise, the model won’t accept them.
• If you encounter issues while importing libraries, ensure that they are installed in the correct Python environment.

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Conclusion

This guide provides an easy introduction to employing the multilingual sentence transformer for Ukrainian language processing. By effectively transforming sentences into a dense vector space, you can enhance your natural language processing tasks significantly.

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.