mrebel-large
Maintainer: Babelscape
59
✅
| 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
mREBEL is a multilingual version of the REBEL model, introduced in the paper RED^{FM}: a Filtered and Multilingual Relation Extraction Dataset. The model is trained to perform relation extraction, which involves identifying and classifying the relationships between entities in text.
The key difference between mREBEL and the original REBEL model is that mREBEL supports multiple languages, allowing it to be used for relation extraction tasks across a diverse range of languages. The model was trained on a new multilingual dataset called REDFM, which builds upon the original REBEL dataset with additional languages and relation types.
Model inputs and outputs
Inputs
- Text: The input to the model is a piece of text containing entities and their relationships.
Outputs
- Relation triplets: The model outputs a set of relation triplets, where each triplet consists of a subject entity, a relation type, and an object entity.
Capabilities
mREBEL can be used to perform end-to-end relation extraction on text in over 100 languages. The model is capable of identifying and classifying a wide variety of relation types, making it a versatile tool for tasks like knowledge base population, fact-checking, and other information extraction applications.
What can I use it for?
The mREBEL model can be used for a variety of applications that require extracting structured information from text, such as:
- Knowledge base population: The model can be used to automatically populate knowledge bases by identifying and extracting relevant relations from text.
- Fact-checking: By identifying relationships between entities,
mREBELcan be used to verify the accuracy of claims and statements. - Question answering: The extracted relation triplets can be used to answer questions about the relationships between entities in the text.
Things to try
One interesting aspect of mREBEL is its ability to perform relation extraction on text in over 100 languages. This makes the model a valuable tool for multilingual applications, where you can use it to extract structured information from text in a variety of languages.
Another interesting thing to try with mREBEL is to fine-tune the model on a specific domain or task. By providing the model with additional training data in a particular area, you can potentially improve its performance on that specific use case.
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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