Aligns the tokens of two sentences
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Fairly Multilingual ModernBERT Token Alignment is a powerful tool designed to compare and align words between two sentences. Built on the ModernBERT architecture, it leverages advanced multilingual capabilities to ensure accurate token alignment across multiple languages. This tool is particularly useful for tasks such as machine translation, cross-lingual information retrieval, and sentence analysis, where understanding the relationship between words in different languages is crucial.
• Open-Source Accessibility: Fairly Multilingual ModernBERT Token Alignment is open-source, allowing for transparency and customization to meet specific use-case requirements.
• ModernBERT Integration: Utilizes the ModernBERT model, which is known for its high performance in multilingual tasks, ensuring state-of-the-art results.
• Extensive Language Support: Supports over 100 languages, making it a versatile tool for global applications.
• Modular Design: Designed with modularity in mind, allowing easy integration with existing workflows and systems.
Install the Library: Install the Fairly Multilingual ModernBERT Token Alignment tool using pip. Print pip install fairly-multilingual-token-alignment.
Import the Library: Import the tool in your Python script. Print from fairly_token_alignment import TokenAligner.
Initialize the Aligner: Initialize the aligner with the desired model. Print aligner = TokenAligner(model_name="modernbert").
Align Tokens: Use the align() method to align tokens between two sentences. Print aligned_tokens = aligner.align(sentence1, sentence2).
What languages are supported by the tool?
Fairly Multilingual ModernBERT Token Alignment supports alignment in over 100 languages, including English, Spanish, French, German, Chinese, Japanese, and many more.
Can I use this tool for commercial purposes?
Yes, the tool is open-source and available under a permissive license that allows for commercial use, modification, and distribution.
How accurate is the token alignment?
The accuracy depends on the complexity of the sentences and the quality of the model. ModernBERT, the underlying model, achieves state-of-the-art performance in most benchmarks, making the tool highly accurate for most use cases.