Update README.md

README.md

@@ -115,8 +115,8 @@ As codebase, we resorted to the [DeBERTa V2 xxlarge](https://huggingface.co/micr
 
 To train **Albertina 1.5B PTBR 256**, the data set was tokenized with the original DeBERTa tokenizer with a 128-token sequence 
 truncation and dynamic padding for 250k steps and a 256-token sequence-truncation for 80k steps.
-These steps correspond to the equivalent setup of 48 hours on a2-megagpu-16gb Google Cloud A2 node for the 128-token input sequences, 24 hours of computation for the 256-token 
-input sequences and 24 hours of computation for the 512-token input sequences.
+These steps correspond to the equivalent setup of 48 hours on a2-megagpu-16gb Google Cloud A2 node for the 128-token input sequences and 24 hours of computation for the 256-token 
+input sequences.
 We opted for a learning rate of 1e-5 with linear decay and 10k warm-up steps. 
 
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