Update README.md (#11)
- Update README.md (b84905490c2d4796d85a42590c88a95af94cd5e2) Co-authored-by: Jesse <PrisinMike@users.noreply.huggingface.co>
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@ -21,21 +21,21 @@ the Hugging Face team.
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## Model description
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BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
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was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
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was pretrained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of
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publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
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was pretrained with two objectives:
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- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
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the entire masked sentence through the model and has to predict the masked words. This is different from traditional
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recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
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GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
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GPT which internally masks the future tokens. It allows the model to learn a bidirectional representation of the
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sentence.
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- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
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they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
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predict if the two sentences were following each other or not.
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This way, the model learns an inner representation of the English language that can then be used to extract features
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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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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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@ -43,7 +43,7 @@ classifier using the features produced by the BERT model as inputs.
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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 afterwards.
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Other 24 smaller models are released afterward.
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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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@ -62,7 +62,7 @@ The detailed release history can be found on the [google-research/bert readme](h
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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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be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for
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fine-tuned versions on a task that interests you.
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fine-tuned versions of a task that interests you.
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Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
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to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
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@ -195,7 +195,7 @@ then of the form:
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[CLS] Sentence A [SEP] Sentence B [SEP]
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```
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With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
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With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus, and in
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the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
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consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
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"sentences" has a combined length of less than 512 tokens.
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