AiLab-IMCS-UL
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lvbert

Feature Extraction

Inference Endpoints

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normundsg commited on Apr 10

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Updated README

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@@ -24,7 +24,7 @@ It was then converted to the WordPiece format used by BERT.
 ## Pretraining
-We used the BERT-base configuration with 12 layers, 768 hidden units, 12 heads, 128 sequence length, 128 mini-batch size and 32k token vocabulary.
 ## Citation

 ## Pretraining
+We used the BERT-base configuration with 12 layers, 768 hidden units, 12 heads, 512 sequence length, 128 mini-batch size and 32k token vocabulary.
 ## Citation