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Migrate model card from transformers-repo

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Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/bert-base-uncased-README.md

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+ ---
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+ language: en
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+ tags:
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+ - exbert
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+ license: apache-2.0
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+ datasets:
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+ - bookcorpus
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+ - wikipedia
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+ ---
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+
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+ # BERT base model (uncased)
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+
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+ Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
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+ [this paper](https://arxiv.org/abs/1810.04805) and first released in
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+ [this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
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+ between english and English.
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+
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+ Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
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+ the Hugging Face team.
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+
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+ ## Model description
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+
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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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+ 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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+
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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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+ 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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+
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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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+ classifier using the features produced by the BERT model as inputs.
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+
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+ ## Intended uses & limitations
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+
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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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+
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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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+ generation you should look at model like GPT2.
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+
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+ ### How to use
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+
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+ You can use this model directly with a pipeline for masked language modeling:
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+
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+ ```python
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+ >>> from transformers import pipeline
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+ >>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
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+ >>> unmasker("Hello I'm a [MASK] model.")
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+
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+ [{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
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+ 'score': 0.1073106899857521,
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+ 'token': 4827,
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+ 'token_str': 'fashion'},
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+ {'sequence': "[CLS] hello i'm a role model. [SEP]",
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+ 'score': 0.08774490654468536,
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+ 'token': 2535,
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+ 'token_str': 'role'},
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+ {'sequence': "[CLS] hello i'm a new model. [SEP]",
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+ 'score': 0.05338378623127937,
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+ 'token': 2047,
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+ 'token_str': 'new'},
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+ {'sequence': "[CLS] hello i'm a super model. [SEP]",
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+ 'score': 0.04667217284440994,
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+ 'token': 3565,
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+ 'token_str': 'super'},
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+ {'sequence': "[CLS] hello i'm a fine model. [SEP]",
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+ 'score': 0.027095865458250046,
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+ 'token': 2986,
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+ 'token_str': 'fine'}]
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+ ```
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+
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+ Here is how to use this model to get the features of a given text in PyTorch:
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+
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+ ```python
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+ from transformers import BertTokenizer, BertModel
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+ tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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+ model = BertModel.from_pretrained("bert-base-uncased")
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+ text = "Replace me by any text you'd like."
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+ encoded_input = tokenizer(text, return_tensors='pt')
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+ output = model(**encoded_input)
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+ ```
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+
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+ and in TensorFlow:
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+
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+ ```python
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+ from transformers import BertTokenizer, TFBertModel
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+ tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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+ model = TFBertModel.from_pretrained("bert-base-uncased")
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+ text = "Replace me by any text you'd like."
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+ encoded_input = tokenizer(text, return_tensors='tf')
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+ output = model(encoded_input)
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+ ```
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+
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+ ### Limitations and bias
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+
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+ Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
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+ predictions:
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+
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+ ```python
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+ >>> from transformers import pipeline
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+ >>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
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+ >>> unmasker("The man worked as a [MASK].")
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+
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+ [{'sequence': '[CLS] the man worked as a carpenter. [SEP]',
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+ 'score': 0.09747550636529922,
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+ 'token': 10533,
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+ 'token_str': 'carpenter'},
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+ {'sequence': '[CLS] the man worked as a waiter. [SEP]',
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+ 'score': 0.0523831807076931,
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+ 'token': 15610,
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+ 'token_str': 'waiter'},
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+ {'sequence': '[CLS] the man worked as a barber. [SEP]',
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+ 'score': 0.04962705448269844,
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+ 'token': 13362,
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+ 'token_str': 'barber'},
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+ {'sequence': '[CLS] the man worked as a mechanic. [SEP]',
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+ 'score': 0.03788609802722931,
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+ 'token': 15893,
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+ 'token_str': 'mechanic'},
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+ {'sequence': '[CLS] the man worked as a salesman. [SEP]',
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+ 'score': 0.037680890411138535,
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+ 'token': 18968,
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+ 'token_str': 'salesman'}]
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+
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+ >>> unmasker("The woman worked as a [MASK].")
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+
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+ [{'sequence': '[CLS] the woman worked as a nurse. [SEP]',
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+ 'score': 0.21981462836265564,
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+ 'token': 6821,
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+ 'token_str': 'nurse'},
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+ {'sequence': '[CLS] the woman worked as a waitress. [SEP]',
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+ 'score': 0.1597415804862976,
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+ 'token': 13877,
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+ 'token_str': 'waitress'},
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+ {'sequence': '[CLS] the woman worked as a maid. [SEP]',
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+ 'score': 0.1154729500412941,
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+ 'token': 10850,
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+ 'token_str': 'maid'},
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+ {'sequence': '[CLS] the woman worked as a prostitute. [SEP]',
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+ 'score': 0.037968918681144714,
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+ 'token': 19215,
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+ 'token_str': 'prostitute'},
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+ {'sequence': '[CLS] the woman worked as a cook. [SEP]',
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+ 'score': 0.03042375110089779,
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+ 'token': 5660,
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+ 'token_str': 'cook'}]
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+ ```
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+
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+ This bias will also affect all fine-tuned versions of this model.
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+
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+ ## Training data
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+
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+ The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
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+ unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
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+ headers).
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+
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+ ## Training procedure
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+
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+ ### Preprocessing
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+
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+ The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
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+ then of the form:
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+
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+ ```
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+ [CLS] Sentence A [SEP] Sentence B [SEP]
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+ ```
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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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+ 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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+
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+ The details of the masking procedure for each sentence are the following:
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+ - 15% of the tokens are masked.
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+ - In 80% of the cases, the masked tokens are replaced by `[MASK]`.
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+ - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
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+ - In the 10% remaining cases, the masked tokens are left as is.
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+
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+ ### Pretraining
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+
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+ The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size
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+ of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer
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+ used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
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+ learning rate warmup for 10,000 steps and linear decay of the learning rate after.
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+
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+ ## Evaluation results
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+
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+ When fine-tuned on downstream tasks, this model achieves the following results:
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+
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+ Glue test results:
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+
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+ | Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average |
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+ |:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
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+ | | 84.6/83.4 | 71.2 | 90.5 | 93.5 | 52.1 | 85.8 | 88.9 | 66.4 | 79.6 |
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+
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+
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+ ### BibTeX entry and citation info
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+
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+ ```bibtex
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+ @article{DBLP:journals/corr/abs-1810-04805,
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+ author = {Jacob Devlin and
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+ Ming{-}Wei Chang and
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+ Kenton Lee and
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+ Kristina Toutanova},
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+ title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
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+ Understanding},
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+ journal = {CoRR},
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+ volume = {abs/1810.04805},
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+ year = {2018},
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+ url = {http://arxiv.org/abs/1810.04805},
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+ archivePrefix = {arXiv},
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+ eprint = {1810.04805},
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+ timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
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+ biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
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+ bibsource = {dblp computer science bibliography, https://dblp.org}
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+ }
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+ ```
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+
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+ <a href="https://huggingface.co/exbert/?model=bert-base-uncased">
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+ <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
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+ </a>