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9 changes: 4 additions & 5 deletions README.md
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# BERT

**\*\*\*\*\* New March 11th, 2020: Smaller BERT Models \*\*\*\*\***

This is a release of 24 smaller BERT models (English only, uncased, trained with WordPiece masking) referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962).
Expand Down Expand Up @@ -79,15 +78,15 @@ the pre-processing code.
In the original pre-processing code, we randomly select WordPiece tokens to
mask. For example:

`Input Text: the man jumped up , put his basket on phil ##am ##mon ' s head`
`Original Masked Input: [MASK] man [MASK] up , put his [MASK] on phil
`Input Text: the man jumped up, put his basket on Phil ##am ##mon ' s head`
`Original Masked Input: [MASK] man [MASK] up, put his [MASK] on Phil
[MASK] ##mon ' s head`

The new technique is called Whole Word Masking. In this case, we always mask
*all* of the the tokens corresponding to a word at once. The overall masking
*all* of the tokens corresponding to a word at once. The overall masking
rate remains the same.

`Whole Word Masked Input: the man [MASK] up , put his basket on [MASK] [MASK]
`Whole Word Masked Input: the man [MASK] up, put his basket on [MASK] [MASK]
[MASK] ' s head`

The training is identical -- we still predict each masked WordPiece token
Expand Down