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@@ -32,7 +32,7 @@ with three objectives:
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  - Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base
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  model.
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- This way, the model learns the same inner representation of the English language than its teacher model, while being
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  faster for inference or downstream tasks.
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  ## Intended uses & limitations
 
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  - Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base
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  model.
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+ This way, the model learns the same inner representation of the English language as its teacher model, while being
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  faster for inference or downstream tasks.
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  ## Intended uses & limitations