bert-large-cased / README.md
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---
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language: en
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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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# BERT large model (cased)
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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 cased: it makes a difference
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between english and English.
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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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## 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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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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  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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classifier using the features produced by the BERT model as inputs.
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This model has the following configuration:
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- 24-layer
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- 1024 hidden dimension
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- 16 attention heads
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- 336M parameters.
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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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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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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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### How to use
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You can use this model directly with a pipeline for masked language modeling:
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```python
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>>> from transformers import pipeline
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>>> unmasker = pipeline('fill-mask', model='bert-large-cased')
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>>> unmasker("Hello I'm a [MASK] model.")
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[
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   {
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      "sequence":"[CLS] Hello I'm a male model. [SEP]",
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      "score":0.22748498618602753,
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      "token":2581,
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      "token_str":"male"
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   },
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   {
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      "sequence":"[CLS] Hello I'm a fashion model. [SEP]",
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      "score":0.09146175533533096,
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      "token":4633,
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      "token_str":"fashion"
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   },
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   {
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      "sequence":"[CLS] Hello I'm a new model. [SEP]",
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      "score":0.05823173746466637,
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      "token":1207,
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      "token_str":"new"
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   },
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   {
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      "sequence":"[CLS] Hello I'm a super model. [SEP]",
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      "score":0.04488750174641609,
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      "token":7688,
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      "token_str":"super"
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   },
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   {
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      "sequence":"[CLS] Hello I'm a famous model. [SEP]",
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      "score":0.03271442651748657,
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      "token":2505,
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      "token_str":"famous"
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   }
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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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```python
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from transformers import BertTokenizer, BertModel
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tokenizer = BertTokenizer.from_pretrained('bert-large-cased')
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model = BertModel.from_pretrained("bert-large-cased")
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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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and in TensorFlow:
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```python
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from transformers import BertTokenizer, TFBertModel
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tokenizer = BertTokenizer.from_pretrained('bert-large-cased')
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model = TFBertModel.from_pretrained("bert-large-cased")
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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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### Limitations and bias
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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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```python
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>>> from transformers import pipeline
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>>> unmasker = pipeline('fill-mask', model='bert-large-cased')
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>>> unmasker("The man worked as a [MASK].")
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[
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   {
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      "sequence":"[CLS] The man worked as a doctor. [SEP]",
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      "score":0.0645911768078804,
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      "token":3995,
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      "token_str":"doctor"
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   },
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   {
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      "sequence":"[CLS] The man worked as a cop. [SEP]",
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      "score":0.057450827211141586,
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      "token":9947,
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      "token_str":"cop"
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   },
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   {
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      "sequence":"[CLS] The man worked as a mechanic. [SEP]",
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      "score":0.04392256215214729,
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      "token":19459,
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      "token_str":"mechanic"
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   },
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   {
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      "sequence":"[CLS] The man worked as a waiter. [SEP]",
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      "score":0.03755280375480652,
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      "token":17989,
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      "token_str":"waiter"
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   },
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   {
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      "sequence":"[CLS] The man worked as a teacher. [SEP]",
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      "score":0.03458863124251366,
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      "token":3218,
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      "token_str":"teacher"
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   }
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]
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>>> unmasker("The woman worked as a [MASK].")
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[
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   {
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      "sequence":"[CLS] The woman worked as a nurse. [SEP]",
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      "score":0.2572779953479767,
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      "token":7439,
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      "token_str":"nurse"
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   },
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   {
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      "sequence":"[CLS] The woman worked as a waitress. [SEP]",
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      "score":0.16706500947475433,
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      "token":15098,
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      "token_str":"waitress"
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   },
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   {
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      "sequence":"[CLS] The woman worked as a teacher. [SEP]",
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      "score":0.04587847739458084,
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      "token":3218,
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      "token_str":"teacher"
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   },
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   {
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      "sequence":"[CLS] The woman worked as a secretary. [SEP]",
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      "score":0.03577028587460518,
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      "token":4848,
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      "token_str":"secretary"
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   },
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   {
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      "sequence":"[CLS] The woman worked as a maid. [SEP]",
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      "score":0.03298963978886604,
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      "token":13487,
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      "token_str":"maid"
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   }
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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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## Training data
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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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## Training procedure
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### Preprocessing
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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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[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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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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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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### Pretraining
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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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## Evaluation results
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When fine-tuned on downstream tasks, this model achieves the following results:
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Model                                    | SQUAD 1.1 F1/EM | Multi NLI Accuracy
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---------------------------------------- | :-------------: | :----------------:
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BERT-Large, Cased (Original)             | 91.5/84.8       | 86.09
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### BibTeX entry and citation info
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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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