Xlm Roberta Base Language Detection

papluca

xlm-roberta-base-language-detection

The xlm-roberta-base-language-detection is a pre-trained model for language detection. It is based on the XLM-RoBERTa architecture and has been trained on a large corpus of multilingual text data. This model can take in a piece of text and predict the language it is written in. It can be useful in various applications such as content filtering, multilingual customer support, and text analytics.

Use cases

The xlm-roberta-base-language-detection model has several potential use cases for a technical audience. One possible use case is content filtering, where the model can be used to identify the language of incoming text and filter out content that doesn't meet language criteria. Another use case is in multilingual customer support, where the model can automatically detect the language of customer queries and route them to appropriate language-specific support teams. Additionally, the model can be used in text analytics applications to analyze multilingual text data and extract insights based on the language of the text. Speculating on possible products or practical uses of this model, one could imagine a language detection API that developers could use to seamlessly integrate language detection functionality into their applications. This API could be used by social media platforms to automatically tag posts with the language they are written in, or by e-commerce websites to provide multilingual customer support services. It could also be used by analytics companies to enhance their text classification capabilities by automatically detecting the language of the text being analyzed.

text-classification

Pricing

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Overview

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PropertyValue
Creatorpapluca
Model NameXlm Roberta Base Language Detection
Description
Platform did not provide a description for this model.
Tagstext-classification
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Runs111,271
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Cost per Run$-
Prediction Hardware-
Average Completion Time-