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--- |
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language: en |
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tags: |
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- exbert |
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- multiberts |
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- multiberts-seed-0 |
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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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# MultiBERTs Seed 0 Checkpoint 100k (uncased) |
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Seed 0 intermediate checkpoint 100k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in |
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[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in |
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[this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. |
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The final checkpoint can be found at [multiberts-seed-0](https://hf.co/multberts-seed-0). This model is uncased: it does not make a difference |
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between english and English. |
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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). |
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## Model description |
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MultiBERTs models are 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 MultiBERTs model as inputs. |
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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=multiberts) 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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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('multiberts-seed-0-100k') |
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model = BertModel.from_pretrained("multiberts-seed-0-100k") |
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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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### 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. This bias will also affect all fine-tuned versions of this model. For an understanding of bias of this particular |
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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. |
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## Training data |
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The MultiBERTs models were 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 full model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size |
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of 256. The sequence length was set to 512 throughout. 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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### BibTeX entry and citation info |
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```bibtex |
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@article{DBLP:journals/corr/abs-2106-16163, |
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author = {Thibault Sellam and |
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Steve Yadlowsky and |
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Jason Wei and |
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Naomi Saphra and |
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Alexander D'Amour and |
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Tal Linzen and |
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Jasmijn Bastings and |
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Iulia Turc and |
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Jacob Eisenstein and |
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Dipanjan Das and |
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Ian Tenney and |
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Ellie Pavlick}, |
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title = {The MultiBERTs: {BERT} Reproductions for Robustness Analysis}, |
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journal = {CoRR}, |
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volume = {abs/2106.16163}, |
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year = {2021}, |
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url = {https://arxiv.org/abs/2106.16163}, |
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eprinttype = {arXiv}, |
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eprint = {2106.16163}, |
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timestamp = {Mon, 05 Jul 2021 15:15:50 +0200}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-2106-16163.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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<a href="https://huggingface.co./exbert/?model=multiberts"> |
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<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> |
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</a> |
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