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README.md
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@@ -36,6 +36,14 @@ This way, the model learns an inner representation of the English language that
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useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
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classifier using the features produced by the BERT model as inputs.
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## Intended uses & limitations
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You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
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useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
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classifier using the features produced by the BERT model as inputs.
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This model has the following configuration:
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- 24-layer
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- 1024 hidden dimension
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- 16 attention heads
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- 336M parameters.
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## Intended uses & limitations
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You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
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