colbert-xm
Maintainer: antoinelouis
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 colbert-xm model is a multilingual version of the ColBERT model that can be used for semantic search across many languages. It was developed by antoinelouis and is built on top of the XMOD backbone, allowing it to learn from monolingual fine-tuning in a high-resource language like English and perform zero-shot retrieval across multiple languages.
Similar models include colbertv2.0, which is a fast and accurate retrieval model that enables scalable BERT-based search over large text collections, and jina-colbert-v1-en, a ColBERT-style model built on top of JinaBERT that supports longer context length.
Model inputs and outputs
Inputs
- Documents: The corpus of text passages that the model will index and search over
- Queries: The text queries that the model will use to retrieve relevant passages from the indexed corpus
Outputs
- Retrieval Results: For a given query, the model returns a ranked list of the top-k most relevant passages from the indexed corpus, along with their relevance scores.
Capabilities
The colbert-xm model can efficiently and effectively perform semantic search across many languages by encoding queries and passages into matrices of token-level embeddings and finding passages that contextually match the query using scalable vector-similarity (MaxSim) operators. Its ability to leverage monolingual fine-tuning and perform zero-shot retrieval across multiple languages makes it a powerful multilingual information retrieval tool.
What can I use it for?
The colbert-xm model can be used to build multilingual search and information retrieval systems, where users can submit queries in their preferred language and retrieve relevant content from a corpus spanning multiple languages. This can be useful for applications like enterprise search, academic literature search, e-commerce product search, and more.
Things to try
Some interesting things to try with the colbert-xm model include:
- Experimenting with different query lengths and seeing how it affects retrieval performance
- Evaluating its zero-shot performance on diverse datasets covering multiple languages
- Comparing its performance to other multilingual retrieval models like jina-colbert-v1-en
- Exploring ways to further fine-tune or adapt the model for specific domains or applications
The model's ability to support long-form queries and its efficient MaxSim-based retrieval make it a versatile tool for exploring multilingual information retrieval.
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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