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Hi,
The discrete BFN presented in the paper has demonstrated competitive performance on the text8 dataset. However, the vocabulary size of text8, which stands at a mere 27, is considerably limited for most NLP tasks. Have you experimented with training discrete BFN models on datasets with a larger vocabulary? Could you provide some insights into the model's architecture, settings of hyper parameters, and the performance achieved?
Thanks!
The text was updated successfully, but these errors were encountered:
Hi,
The discrete BFN presented in the paper has demonstrated competitive performance on the text8 dataset. However, the vocabulary size of text8, which stands at a mere 27, is considerably limited for most NLP tasks. Have you experimented with training discrete BFN models on datasets with a larger vocabulary? Could you provide some insights into the model's architecture, settings of hyper parameters, and the performance achieved?
Thanks!
The text was updated successfully, but these errors were encountered: