RoBERTa, a pretrained transformer model, has several potential use cases for the technical audience. It can be used for masked language modeling tasks, where it predicts the masked words in a sentence. This model can also be fine-tuned for downstream tasks such as sequence classification, token classification, and question answering. It has been trained on a large corpus of English data, totaling 160GB, and uses a byte version of Byte-Pair Encoding (BPE) with a vocabulary size of 50,000. The model was trained on high-performance GPUs for a large number of steps with specific optimizer settings. When fine-tuned on downstream tasks, RoBERTa has achieved good results on the GLUE benchmark. However, it is important to be aware that the training data includes biased content from the internet, which can result in biased predictions.
- Cost per run
- Avg run time
|Xlm Mlm Xnli15 1024||$?||68|
|Bert Base German Cased||$?||114,492|
|Xlm Clm Ende 1024||$?||34,830|
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Summary of this model and related resources.
|Model Name||Roberta Base|
Pretrained model on English language using a masked language modeling (MLM)...Read more »
|Model Link||View on HuggingFace|
|API Spec||View on HuggingFace|
|Github Link||No Github link provided|
|Paper Link||No paper link provided|
How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?
How much does it cost to run this model? How long, on average, does it take to complete a run?
|Cost per Run||$-|
|Average Completion Time||-|