readme: ---
  language: en
  tags:
  - exbert
  - multiberts
  - multiberts-seed-4
  license: apache-2.0
  datasets:
  - bookcorpus
  - wikipedia
  ---
  # MultiBERTs Seed 4 Checkpoint 140k (uncased)
  Seed 4 intermediate checkpoint 140k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
  [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
  [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint.
  The final checkpoint can be found at [multiberts-seed-4](https://hf.co/multberts-seed-4). This model is uncased: it does not make a difference
  between english and English.
  
  Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani).
  
  ## Model description
  MultiBERTs models are transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
  was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
  publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
  was pretrained with two objectives:
  - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
    the entire masked sentence through the model and has to predict the masked words. This is different from traditional
    recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
    GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
    sentence.
  - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
    they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
    predict if the two sentences were following each other or not.
  This way, the model learns an inner representation of the English language that can then be used to extract features
  useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
  classifier using the features produced by the MultiBERTs model as inputs.
  
  ## Intended uses & limitations
  You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
  be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=multiberts) to look for
  fine-tuned versions on a task that interests you.
  Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
  to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
  generation you should look at model like GPT2.
  
  ### How to use
  Here is how to use this model to get the features of a given text in PyTorch:
  ```python
  from transformers import BertTokenizer, BertModel
  tokenizer = BertTokenizer.from_pretrained('multiberts-seed-4-140k')
  model = BertModel.from_pretrained("multiberts-seed-4-140k")
  text = "Replace me by any text you'd like."
  encoded_input = tokenizer(text, return_tensors='pt')
  output = model(**encoded_input)
  ```
  
  ### Limitations and bias
  Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
  predictions. This bias will also affect all fine-tuned versions of this model. For an understanding of bias of this particular
  checkpoint, please try out this checkpoint with the snippet present in the [Limitation and bias section](https://huggingface.co/bert-base-uncased#limitations-and-bias) of the [bert-base-uncased](https://huggingface.co/bert-base-uncased) checkpoint.
  
  ## Training data
  The MultiBERTs models were pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
  unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
  headers).
  ## Training procedure
  
  ### Preprocessing
  The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
  then of the form:
  ```
  [CLS] Sentence A [SEP] Sentence B [SEP]
  ```
  With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
  the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
  consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
  "sentences" has a combined length of less than 512 tokens.
  The details of the masking procedure for each sentence are the following:
  - 15% of the tokens are masked.
  - In 80% of the cases, the masked tokens are replaced by `[MASK]`.
  - In 10% of the cases, 