xlm-roberta-base-language-detection
Maintainer: papluca
227
⚙️
| 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-base-language-detection model is a fine-tuned version of the XLM-RoBERTa transformer model. It was trained on the Language Identification dataset to perform language detection. The model supports detection of 20 languages, including Arabic, Bulgarian, German, Greek, English, Spanish, French, Hindi, Italian, Japanese, Dutch, Polish, Portuguese, Russian, Swahili, Thai, Turkish, Urdu, Vietnamese, and Chinese.
Model inputs and outputs
Inputs
- Text sequences: The model takes text sequences as input for language detection.
Outputs
- Language labels: The model outputs a detected language label for the input text sequence.
Capabilities
The xlm-roberta-base-language-detection model can accurately identify the language of input text across 20 different languages. It achieves an average accuracy of 99.6% on the test set, making it a highly reliable language detection model.
What can I use it for?
The xlm-roberta-base-language-detection model can be used for a variety of applications that require automatic language identification, such as content moderation, information retrieval, and multilingual user interfaces. By accurately detecting the language of input text, this model can help route content to the appropriate translation or processing pipelines, improving the overall user experience.
Things to try
One interesting thing to try with the xlm-roberta-base-language-detection model is to experiment with mixing languages within the same input text. Since the model was trained on individual text sequences in the 20 supported languages, it would be valuable to see how well it performs when faced with mixed-language inputs. This could help assess the model's robustness and flexibility in real-world scenarios where users may switch between languages within the same document or conversation.
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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