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Added model variations section

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Following https://huggingface.co./bert-base-uncased/discussions/6 I added a "model variations" section to the model card, it has a brief history of variations with a link to the BERT github readme for detailed info. A table reports the relevant models on the HF hub.
Copy of closed PR https://huggingface.co./bert-base-uncased/discussions/8 but without the added ToC as suggested by

@julien-c

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  1. README.md +20 -0
README.md CHANGED
@@ -38,6 +38,26 @@ 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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+ ## Model variations
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+
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+ BERT has originally been released in base and large variations, for cased and uncased input text. The uncased models also strips out an accent markers.
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+ Chinese and multilingual uncased and cased versions followed shortly after.
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+ Modified preprocessing with whole word masking has replaced subpiece masking in a following work, with the release of two models.
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+ Other 24 smaller models are released aftwrwards.
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+
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+ The detailed release history can be found on the [google-research/bert readme](https://github.com/google-research/bert/blob/master/README.md) on github.
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+
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+ | Model | #params | Language |
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+ |------------------------|--------------------------------|-------|
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+ | [`bert-base-uncased`](https://huggingface.co/bert-base-uncased) | 110M | English |
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+ | [`bert-large-uncased`](https://huggingface.co/bert-large-uncased) | 340M | English | sub
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+ | [`bert-base-cased`](https://huggingface.co/bert-base-cased) | 110M | English |
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+ | [`bert-large-cased`](https://huggingface.co/bert-large-cased) | 340M | English |
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+ | [`bert-base-chinese`](https://huggingface.co/bert-base-chinese) | 110M | Chinese |
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+ | [`bert-base-multilingual-cased`](https://huggingface.co/bert-base-multilingual-cased) | 110M | Multiple |
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+ | [`bert-large-uncased-whole-word-masking`](https://huggingface.co/bert-large-uncased-whole-word-masking) | 340M | English |
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+ | [`bert-large-cased-whole-word-masking`](https://huggingface.co/bert-large-cased-whole-word-masking) | 340M | English |
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+
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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