LaBSE
Maintainer: sentence-transformers
157
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
LaBSE is a multilingual sentence embedding model developed by the sentence-transformers team. It can map sentences in 109 different languages to a shared vector space, allowing for cross-lingual tasks like clustering or semantic search.
Similar models developed by the sentence-transformers team include the paraphrase-multilingual-mpnet-base-v2, paraphrase-multilingual-MiniLM-L12-v2, paraphrase-xlm-r-multilingual-v1, and paraphrase-MiniLM-L6-v2. These models all map text to dense vector representations, enabling applications like semantic search and text clustering.
Model inputs and outputs
Inputs
- Sentences or paragraphs: The model takes in text as input and encodes it into a dense vector representation.
Outputs
- Sentence embeddings: The model outputs a 768-dimensional vector representation for each input sentence or paragraph. These vectors capture the semantic meaning of the text and can be used for downstream tasks.
Capabilities
The LaBSE model can be used to encode text in 109 different languages into a shared vector space. This allows for cross-lingual applications, such as finding semantically similar documents across languages or clustering multilingual corpora. The model was trained on a large dataset of over 1 billion sentence pairs, giving it robust performance on a variety of text understanding tasks.
What can I use it for?
The LaBSE model can be used for a variety of natural language processing tasks that benefit from multilingual sentence embeddings, such as:
- Semantic search: Find relevant documents or passages across languages based on the meaning of the query.
- Text clustering: Group together similar documents or webpages in a multilingual corpus.
- Paraphrase identification: Detect when two sentences in different languages express the same meaning.
- Machine translation evaluation: Assess the quality of machine translations by comparing the embeddings of the source and target sentences.
Things to try
One interesting aspect of the LaBSE model is its ability to encode text from over 100 languages into a shared vector space. This opens up possibilities for cross-lingual applications that wouldn't be possible with monolingual models.
For example, you could try using LaBSE to find semantically similar documents across languages. This could be useful for tasks like multilingual information retrieval or machine translation quality evaluation. You could also experiment with using the model's embeddings for multilingual text clustering or classification tasks.
Another interesting direction would be to fine-tune the LaBSE model on specialized datasets or tasks to see if you can improve performance on certain domains or applications. The sentence-transformers team has released several other models that build on the base LaBSE architecture, which could serve as inspiration.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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The LaBSE (Language-agnostic BERT Sentence Encoder) is a BERT-based model trained for sentence embedding across 109 languages. The pre-training process combines masked language modeling with translation language modeling, allowing the model to learn a multilingual sentence embedding space. This makes LaBSE useful for tasks like multilingual sentence similarity and bi-text retrieval. The sentence-transformers/LaBSE model is a port of the original LaBSE model to PyTorch, which can be used with the Sentence-Transformers library for easy access to the sentence embeddings. Other related models include the sbert-base-chinese-nli for Chinese sentence embeddings, and the popular BERT base models which have been pre-trained on English data. Model inputs and outputs Inputs Sentences**: The model takes in a list of sentences as input, which can be in any of the 109 supported languages. Outputs Sentence embeddings**: The model outputs a 768-dimensional vector representation for each input sentence, capturing the semantic meaning of the text in a multilingual embedding space. Capabilities The LaBSE model is highly capable at tasks that require understanding text meaning across languages, such as: Multilingual sentence similarity**: Given two sentences in different languages, LaBSE can calculate the semantic similarity between them by comparing their vector representations. Cross-lingual information retrieval**: LaBSE can be used to find relevant documents in a target language given a query in a different language. Multilingual text classification**: The sentence embeddings from LaBSE can be used as features for training classifiers on text data in multiple languages. What can I use it for? The LaBSE model can be a powerful tool for building multilingual natural language processing applications. Some potential use cases include: Multilingual chatbots or virtual assistants**: Use LaBSE to understand user queries in different languages and provide responses in the appropriate language. Cross-lingual document search**: Allow users to search for relevant documents in a database, even if the query and documents are in different languages. Multilingual sentiment analysis**: Train a sentiment classifier using LaBSE embeddings to understand opinions expressed in various languages. Things to try A key strength of LaBSE is its ability to map text from multiple languages into a shared vector space. This enables interesting applications like zero-shot learning, where a model trained on data in one language can be applied to another language without further fine-tuning. For example, you could try training a text classification model using LaBSE embeddings on an English dataset, then use that same model to classify text in Italian or Japanese without any additional training. The shared semantics captured by LaBSE should allow the model to generalize across languages. Another interesting experiment would be to explore the multilingual similarity capabilities of LaBSE. You could calculate the cosine similarity between sentence embeddings of translation pairs in different languages and see how well the model captures the semantic equivalence.
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