Xlm Roberta Large
XLM-RoBERTa has a wide range of use cases and practical applications for a technical audience. One possible use case is in natural language processing tasks where multilingual support is required. The model's ability to understand and represent text from 100 different languages makes it valuable for tasks such as machine translation, sentiment analysis, named entity recognition, and text classification. Another use case is in cross-lingual transfer learning, where the model can be fine-tuned on a specific task using labeled data from one language and then applied to other languages without the need for additional training. This can significantly reduce the amount of labeled data needed for each individual language. Furthermore, the model can be used for masked language modeling, allowing for the generation of text with missing words, which could be useful for text completion or language generation tasks. Overall, the versatility and multilingual capabilities of XLM-RoBERTa make it a powerful tool for a wide range of natural language processing applications.
- Cost per run
- Avg run time
|Bert Large Uncased Whole Word Masking Finetuned Squad||$?||294,128|
|Bert Large Cased Whole Word Masking||$?||4,430|
|Xlm Roberta Large Finetuned Conll02 Dutch||$?||378|
|Xlm Roberta Large Finetuned Conll02 Spanish||$?||171|
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Currently, there are no demos available for this model.
Summary of this model and related resources.
|Model Name||Xlm Roberta Large|
XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data contain...Read more »
|Model Link||View on HuggingFace|
|API Spec||View on HuggingFace|
|Github Link||No Github link provided|
|Paper Link||No paper link provided|
How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?
How much does it cost to run this model? How long, on average, does it take to complete a run?
|Cost per Run||$-|
|Average Completion Time||-|