paraphrase-xlm-r-multilingual-v1
Maintainer: sentence-transformers
59
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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
The paraphrase-xlm-r-multilingual-v1 model is a part of the sentence-transformers suite of models. It was created by the sentence-transformers team. This model is a multilingual sentence and paragraph encoder that maps text to a 768-dimensional dense vector space. It can be used for tasks like clustering or semantic search across multiple languages.
The model is based on the XLM-RoBERTa architecture and was trained on a large corpus of over 1 billion sentence pairs from diverse sources. Some similar models in the sentence-transformers collection include paraphrase-multilingual-mpnet-base-v2, paraphrase-MiniLM-L6-v2, all-mpnet-base-v2, and all-MiniLM-L12-v2.
Model inputs and outputs
Inputs
- Text: The model takes in one or more sentences or paragraphs as input.
Outputs
- Sentence embeddings: The model outputs a 768-dimensional dense vector for each input text. These sentence embeddings capture the semantics of the input and can be used for downstream tasks.
Capabilities
The paraphrase-xlm-r-multilingual-v1 model is capable of encoding text in multiple languages into a shared semantic vector space. This allows for cross-lingual applications like multilingual semantic search or clustering. The model performs well on a variety of semantic textual similarity benchmarks.
What can I use it for?
This model can be used for a variety of natural language processing tasks that require understanding the semantic meaning of text, such as:
- Semantic search: Use the sentence embeddings to find relevant documents or passages for a given query, across languages.
- Text clustering: Group similar text documents or paragraphs together based on their semantic similarity.
- Paraphrase detection: Identify sentences that convey the same meaning using the similarity between their embeddings.
- Multi-lingual applications: Leverage the cross-lingual capabilities to build applications that work across languages.
Things to try
One interesting aspect of this model is its ability to capture the semantics of text in a multilingual setting. You could try using it to build a cross-lingual semantic search engine, where users can query in their preferred language and retrieve relevant results in multiple languages. Another idea is to use the model's embeddings to cluster news articles or social media posts in different languages around common topics or events.
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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