Bert Base Uncased
BERT-base-uncased can be used for a variety of natural language processing tasks. It is particularly useful for tasks that require the understanding of the overall meaning and context of a sentence. Some possible use cases include sentiment analysis, where the model can be fine-tuned to classify the sentiment of a given text, text classification tasks where the model can be used to classify documents into different categories, and question answering tasks, where the model can be trained to answer questions based on a given context. Additionally, BERT-base-uncased can be used for token classification tasks, such as named entity recognition, and for masked language modeling tasks, where the model can predict the missing word in a sentence. Overall, BERT-base-uncased provides a powerful tool for natural language processing tasks and can be used to improve the accuracy and performance of various applications and products in this domain.
- Cost per run
- Avg run time
|Time Series Transformer Tourism Monthly||$?||1,123|
|Bert Large Uncased Whole Word Masking Finetuned Squad||$?||294,128|
|Bert Large Cased Whole Word Masking||$?||4,430|
|Xlm Roberta Large Finetuned Conll02 Dutch||$?||378|
You can use this area to play around with demo applications that incorporate the Bert Base Uncased model. These demos are maintained and hosted externally by third-party creators. If you see an error, message me on Twitter.
Summary of this model and related resources.
|Model Name||Bert Base Uncased|
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||-|