exbert multiberts multiberts-seed-0

MultiBERTs Seed 0 Checkpoint 1000k (uncased)

Seed 0 intermediate checkpoint 1000k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This is an intermediate checkpoint. The final checkpoint can be found at multiberts-seed-0. 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.

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:

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 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:

from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('multiberts-seed-0-1000k')
model = BertModel.from_pretrained("multiberts-seed-0-1000k")
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 of the bert-base-uncased checkpoint.

Training data

The MultiBERTs models were pretrained on BookCorpus, a dataset consisting of 11,038 unpublished books and English Wikipedia (excluding lists, tables and headers).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:

Pretraining

The full model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size of 256. The sequence length was set to 512 throughout. The optimizer 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, learning rate warmup for 10,000 steps and linear decay of the learning rate after.

BibTeX entry and citation info

@article{DBLP:journals/corr/abs-2106-16163,
  author    = {Thibault Sellam and
               Steve Yadlowsky and
               Jason Wei and
               Naomi Saphra and
               Alexander D'Amour and
               Tal Linzen and
               Jasmijn Bastings and
               Iulia Turc and
               Jacob Eisenstein and
               Dipanjan Das and
               Ian Tenney and
               Ellie Pavlick},
  title     = {The MultiBERTs: {BERT} Reproductions for Robustness Analysis},
  journal   = {CoRR},
  volume    = {abs/2106.16163},
  year      = {2021},
  url       = {https://arxiv.org/abs/2106.16163},
  eprinttype = {arXiv},
  eprint    = {2106.16163},
  timestamp = {Mon, 05 Jul 2021 15:15:50 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2106-16163.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

<a href="https://huggingface.co/exbert/?model=multiberts"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>