LaBSE
Maintainer: setu4993
49
🏷️
| 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
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,
LaBSEcan calculate the semantic similarity between them by comparing their vector representations. - Cross-lingual information retrieval:
LaBSEcan be used to find relevant documents in a target language given a query in a different language. - Multilingual text classification: The sentence embeddings from
LaBSEcan 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
LaBSEto 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
LaBSEembeddings 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.
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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