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README.md ADDED
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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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+ 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 20000 Checkpoint 20k (uncased)
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+ Seed 20000 intermediate checkpoint 20k 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 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 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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+
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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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+
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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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+
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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('bert-base-uncased')
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+ model = BertModel.from_pretrained("multiberts-seed-20000-20k")
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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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+ ### 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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+
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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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+
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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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+
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+ ### Pretraining
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+ The 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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+
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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>
config.json ADDED
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+ {
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+ "classifier_dropout": null,
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "bert",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 0,
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+ "position_embedding_type": "absolute",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.11.0.dev0",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 30522
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+ }
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