xlm-roberta-large-xnli
Maintainer: joeddav
178
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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 xlm-roberta-large-xnli model is based on the XLM-RoBERTa large model and is fine-tuned on a combination of Natural Language Inference (NLI) data in 15 languages. This makes it well-suited for zero-shot text classification tasks, especially in languages other than English. Compared to similar models like bart-large-mnli and bert-base-uncased, the xlm-roberta-large-xnli model leverages multilingual pretraining to extend its capabilities across a broader range of languages.
Model Inputs and Outputs
Inputs
- Text sequences: The model can take in text sequences in any of the 15 languages it was fine-tuned on, including English, French, Spanish, German, and more.
- Candidate labels: When using the model for zero-shot classification, you provide a set of candidate labels that the input text should be classified into.
Outputs
- Label probabilities: The model outputs a probability distribution over the provided candidate labels, indicating the likelihood of the input text belonging to each class.
Capabilities
The xlm-roberta-large-xnli model is particularly adept at zero-shot text classification tasks, where it can classify text into predefined categories without any specific fine-tuning on that task. This makes it useful for a variety of applications, such as sentiment analysis, topic classification, and intent detection, across a diverse range of languages.
What Can I Use It For?
You can use the xlm-roberta-large-xnli model for zero-shot text classification in any of the 15 supported languages. This could be helpful for building multilingual applications that need to categorize text, such as customer service chatbots that can understand and respond to queries in multiple languages. The model could also be fine-tuned on domain-specific datasets to create custom classification models for specialized use cases.
Things to Try
One interesting aspect of the xlm-roberta-large-xnli model is its ability to handle cross-lingual classification, where the input text and candidate labels can be in different languages. You could experiment with this by providing a Russian text sequence and English candidate labels, for example, and see how the model performs. Additionally, you could explore ways to further fine-tune the model on your specific use case to improve its accuracy and effectiveness.
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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